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1.
The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

2.
The US Army ERDC CRREL and the US Department of Agriculture Natural Resources Conservation Service developed a square electronic snow water equivalent (e‐SWE) sensor as an alternative to using fluid‐filled snow pillows to measure SWE. The sensors consist of a centre panel to measure SWE and eight outer panels to buffer edge stress concentrations. Seven 3 m square e‐SWE sensors were installed in five different climate zones. During the 2011–2012 winter, 1.8 and 1.2 m square e‐SWE sensors were installed and operated in Oregon. With the exception of New York State and Newfoundland, the e‐SWE sensors accurately measured SWE, with R2 values between the sensor and manual SWE measurements of between 0.86 and 0.98. The e‐SWE sensor at Hogg Pass, Oregon, accurately measured SWE during the past 8 years of operations. In the thin, icy snow of New York during midwinter 2008–2009, the e‐SWE sensors overmeasured SWE because of edge stress concentrations associated with strong icy layers and a shallow snow cover. The New York e‐SWE sensors' measurement accuracy improved in spring 2009 and further improved during the 2011–2012 winter with operating experience. At Santiam Junction, measured SWE from the 1.8 and 1.2 m square e‐SWE sensors agreed well with the snow pillow, 3 m square e‐SWE sensor, and manual SWE measurements until February 2013, when dust and gravel blew onto the testing area resulting in anomalous measurements. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.  相似文献   

3.
Kyuhyun Byun  Minha Choi 《水文研究》2014,28(7):3173-3184
Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

4.
Comparisons between snow water equivalent (SWE) and river discharge estimates are important in evaluating the SWE fields and to our understanding of linkages in the freshwater cycle. In this study, we compared SWE drawn from land surface models and remote sensing observations with measured river discharge (Q) across 179 Arctic river basins. Over the period 1988‐2000, basin‐averaged SWE prior to snowmelt explains a relatively small (yet statistically significant) fraction of interannual variability in spring (April–June) Q, as assessed using the coefficient of determination (R2). Averaged across all basins, mean R2s vary from 0·20 to 0·28, with the best agreement noted for SWE drawn from a simulation with the Pan‐Arctic Water Balance Model (PWBM) forced with data from the European Centre for Medium‐Range Weather‐Forecasts (ECMWF) Re‐analysis (ERA‐40). Variability and magnitude in SWE derived from Special Sensor Microwave Imager (SSM/I) data are considerably lower than the variability and magnitude in SWE drawn from the land surface models, and generally poor agreement is noted between SSM/I SWE and spring Q. We find that the SWE versus Q comparisons are no better when alternate temporal integrations–using an estimate of the timing in basin thaw–are used to define pre‐melt SWE and spring Q. Thus, a majority of the variability in spring discharge must arise from factors other than basin snowpack water storage. This study demonstrates how SWE estimated from remote sensing observations, or general circulation models (GCMs), can be evaluated effectively using monthly discharge data or SWE from a hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

5.
Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km2), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.  相似文献   

6.
Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal, was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km2), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.  相似文献   

7.
Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

8.
Snow water equivalent (SWE) estimates at the end of the winter season have been compared for the 2002–2006 period in a 200 km2 mountainous area in Switzerland, using three different models. The first model, ALPINE3D, is a physically based process-oriented model, which solves the snowpack energy and mass balance equations. The other two models, SWE-SEM and HS-SWE, are statistical algorithms interpolating snow data on a grid. While SWE-SEM interpolates local estimates of SWE, HS-SWE converts interpolated snow depth maps into maps of SWE using a regionally-calibrated conversion model. We discuss similarities and differences among the models’ results, both in terms of total volume, and spatial distribution of SWE. The comparison shows a general good agreement of the results of the three models, with a mean difference in the total volumes between the two statistical models of ∼8%, and between the physical model and the statistical ones of ∼−3% to −10%.  相似文献   

9.
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   

10.
Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm3 was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.  相似文献   

11.
This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2 km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.  相似文献   

12.
In non-forested mountain regions, wind plays a dominant role in determining snow accumulation and melt patterns. A new, computationally efficient algorithm for distributing the complex and heterogeneous effects of wind on snow distributions was developed. The distribution algorithm uses terrain structure, vegetation, and wind data to adjust commonly available precipitation data to simulate wind-affected accumulations. This research describes model development and application in three research catchments in the Reynolds Creek Experimental Watershed in southwest Idaho, USA. All three catchments feature highly variable snow distributions driven by wind. The algorithm was used to derive model forcings for Isnobal, a mass and energy balance distributed snow model. Development and initial testing took place in the Reynolds Mountain East catchment (0.36 km2) where R2 values for the wind-affected snow distributions ranged from 0.50 to 0.67 for four observation periods spanning two years. At the Upper Sheep Creek catchment (0.26 km2) R2 values for the wind-affected model were 0.66 and 0.70. These R2 values matched or exceeded previously published cross-validation results from regression-based statistical analyses of snow distributions in similar environments. In both catchments the wind-affected model accurately located large drift zones, snow-scoured slopes, and produced melt patterns consistent with observed streamflow. Models that did not account for wind effects produced relatively homogenous SWE distributions, R2 values approaching 0.0, and melt patterns inconsistent with observed streamflow. The Dobson Creek (14.0 km2) application incorporated elevation effects into the distribution routine and was conducted over a two-dimensional grid of 6.67 × 105 pixels. Comparisons with satellite-derived snow-covered-area again demonstrated that the model did an excellent job locating regions with wind-affected snow accumulations. This final application demonstrated that the computational efficiency and modest data requirements of this approach are ideally suited for large-scale operational applications.  相似文献   

13.
Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km2 sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R2. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R2) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R2 (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R2 scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R2 skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R2. The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

14.
Many plot‐scale studies have shown that snow‐cover dynamics in forest gaps are distinctly different from those in open and continuously forested areas, and forest gaps have the potential to alter the magnitude and timing of snowmelt. However, the watershed‐level impacts of canopy gap treatment on streamflows are largely unknown. Here, we present the first research that explicitly assesses the impact of canopy gaps on seasonal streamflows and particularly late‐season low flows at the watershed scale. To explicitly model forest–snow interactions in canopy gaps, we made major enhancements to a widely used distributed hydrologic model, distributed hydrology soil vegetation model, with a canopy gap component that represents physical processes of snowpack evolution in the forest gap separately from the surrounding forest on the subgrid scale (within a grid typically 10–150 m). The model predicted snow water equivalent using the enhanced distributed hydrology soil vegetation model showed good agreement (R2 > 0.9) with subhourly snow water equivalent measurements collected from open, forested, and canopy gap sites in Idaho, USA. Compared with the original model that does not account for interactions between gaps and surrounding forest, the enhanced model predicted notably later melt in small‐ to medium‐size canopy gaps (the ratio of gap radius (r) to canopy height (h) ≤ 1.2), and snow melt rates exhibited great sensitivity to changing gap size in medium‐size gaps (0.5 ≤ r/h ≤ 1.2). We demonstrated the watershed‐scale implications of canopy gaps on streamflow in the snow‐dominated Chiwawa watershed, WA, USA. With 24% of the watershed drainage area (about 446 km2) converted to gaps of 60 m diameter, the mean annual 7‐day low flow was increased by 19.4% (i.e., 0.37 m3/s), and the mean monthly 7‐day low flows were increased by 13.5% (i.e., 0.26 m3/s) to 40% (i.e., 1.76 m3/s) from late summer through fall. Lastly, in practical implementation of canopy gaps with the same total gap areas, a greater number of distributed small gaps can have greater potential for longer snow retention than a smaller number of large gaps.  相似文献   

15.
Dissolved organic matter (DOM) quality and quantity is not measured routinely in‐situ limiting our ability to quantify DOM process dynamics. This is problematic given legislative obligations to determine event based variability; however, recent advances in field deployable optical sensing technology provide the opportunity to address this problem. In this paper, we outline a new approach for in‐situ quantification of DOM quantity (Dissolved Organic Carbon: DOC) and a component of quality (Biochemical Oxygen Demand: BOD) using a multi‐wavelength, through‐flow fluorescence sensor. The sensor measured tryptophan‐like (Peak T) and humic‐like (Peak C) fluorescence, alongside water temperature and turbidity. Laboratory derived coefficients were developed to compensate for thermal quenching and turbidity interference (i.e., light attenuation and scattering). Field tests were undertaken on an urban river with ageing wastewater and stormwater infrastructure (Bourn Brook; Birmingham, UK). Sensor output was validated against laboratory determinations of DOC and BOD collected by discrete grab sampling during baseflow and stormflow conditions. Data driven regression models were then compared to laboratory correction methods. A combination of temperature and turbidity compensated Peak T and Peak C was found to be a good predictor of DOC concentration (R2 = 0.92). Conversely, using temperature and turbidity correction coefficients provided low predictive power for BOD (R2 = 0.46 and R2 = 0.51, for Peak C and T, respectively). For this study system, turbidity appeared to be a reasonable proxy for BOD, R2 = 0.86. However, a linear mixed effect model with temperature compensated Peak T and turbidity provided a robust BOD prediction (R2 = 0.95). These findings indicate that with careful initial calibration, multi‐wavelength fluorescence, coupled with turbidity, and temperature provides a feasible proxy for continuous, in‐situ measurement of DOC concentration and BOD. This approach represents a cost effective monitoring solution, particularly when compared to UV – absorbance sensors and DOC analysers, and could be readily adopted for research and industrial applications.  相似文献   

16.
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   

17.
During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km2 domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

18.
High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R2 = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R2 = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.  相似文献   

19.
The hydrology of boreal regions is strongly influenced by seasonal snow accumulation and melt. In this study, we compare simulations of snow water equivalent (SWE) and streamflow by using the hydrological model HYDROTEL with two contrasting approaches for snow modelling: a mixed degree‐day/energy balance model (small number of inputs, but several calibration parameters needed) and the thermodynamic model CROCUS (large number of inputs, but no calibration parameter needed). The study site, in Northern Quebec, Canada was equipped with a ground‐based gamma ray sensor measuring the SWE continuously for 5 years in a small forest clearing. The first simulation of CROCUS showed a tendency to underestimate SWE, attributable to bias in the meteorological inputs. We found that it was appropriate to use a threshold of 2 °C to separate rain and snow. We also applied a correction to account for snowfall undercatch by the precipitation gauge. After these modifications to the input dataset, we noticed that CROCUS clearly overestimated the SWE, likely as a result of not including loss in SWE because of blowing snow sublimation and relocation. To correct this, we included into CROCUS a simple parameterisation effective after a certain wind speed threshold, after which the thermodynamic model performed much better than the traditional mixed degree‐day/energy balance model. HYDROTEL was then used to simulate streamflow with both snow models. With CROCUS, the main peak flow could be captured, but the second peak because of delayed snowmelt from forested areas could not be reproduced due to a lack of sub‐canopy radiation data to feed CROCUS. Despite the relative homogeneity of the boreal landscape, data inputs from each land cover type are needed to generate satisfying simulation of the spring runoff. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

20.
Reliable estimation of the volume and timing of snowmelt runoff is vital for water supply and flood forecasting in snow‐dominated regions. Snowmelt is often simulated using temperature‐index (TI) models due to their applicability in data‐sparse environments. Previous research has shown that a modified‐TI model, which uses a radiation‐derived proxy temperature instead of air temperature as its surrogate for available energy, can produce more accurate snow‐covered area (SCA) maps than a traditional TI model. However, it is unclear whether the improved SCA maps are associated with improved snow water equivalent (SWE) estimation across the watershed or improved snowmelt‐derived streamflow simulation. This paper evaluates whether a modified‐TI model produces better streamflow estimates than a TI model when they are used within a fully distributed hydrologic model. It further evaluates the performance of the two models when they are calibrated using either point SWE measurements or SCA maps. The Senator Beck Basin in Colorado is used as the study site because its surface is largely bedrock, which reduces the role of infiltration and emphasizes the role of the SWE pattern on streamflow generation. Streamflow is simulated using both models for 6 years. The modified‐TI model produces more accurate streamflow estimates (including flow volume and peak flow rate) than the TI model, likely because the modified‐TI model better reproduces the SWE pattern across the watershed. Both models also produce better performance when calibrated with SCA maps instead of point SWE data, likely because the SCA maps better constrain the space‐time pattern of SWE.  相似文献   

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