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1.
Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post-storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time-lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule-based direct insertion method. Interception determined using density estimates from the Hedstrom-Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree-measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time-lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.  相似文献   

2.
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   

3.
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   

4.
Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R2 of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R2 = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R2 = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R2 = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.  相似文献   

5.
We investigate the problem of balancing model complexity and input data requirements in snow hydrology. For this purpose, we analyze the performance of two models of different complexity in estimating variables of interest in snow hydrology applications. These are snow depth, bulk snow density, snow water equivalent and snowmelt run‐off. We quantify the differences between data and model prediction using 18 years of measurements from an experimental site in the French Alps (Col de Porte, 1325 m AMSL). The models involved in this comparison are a one‐layer temperature‐index model (HyS) and a multilayer model (Crocus). Results show that the expected loss in performance in the one‐layer temperature‐index model with respect to the multilayer model is low when considering snow depth, snow water equivalent and bulk snow density. As for run‐off, the comparison returns less clear indications for identification of a balance. In particular, differences between the models' prediction and data with an hourly resolution are higher when considering the Crocus model than the HyS model. However, Crocus is better at reproducing sub‐daily cycles in this variable. In terms of daily run‐off, the multilayer physically based model seems to be a better choice, while results in terms of cumulative run‐off are comparable. The better reproduction of daily and sub‐daily variability of run‐off suggests that use of the multilayer model may be preferable for this purpose. Variation in performance is discussed as a function of both the calibration solution chosen and the time of year. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

6.
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   

7.
It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.  相似文献   

8.
Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r2 by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

9.
ABSTRACT

A hydrological sampling scheme should satisfy the purpose for which the samples are being collected, allowing for the variability of the samples in time and space. Snow measurements are commonly taken to provide estimates of the mean water equivalent over an area. A criterion for selecting a snow sampling scheme for shallow snowpacks is expressed in terms of the standard error of the mean water equivalent as a function of the means, variances, and sample sizes of snow depth and snow density. Analysis of data from the Blue Springs Representative Basin, Ontario, Canada, reveals relatively minor increases in the standard error term as the number of snow density samples is greatly reduced.  相似文献   

10.
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   

11.
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   

12.
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   

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.
Transportation, sublimation and accumulation of snow dominate snow cover development in the Arctic and produce episodic high evaporative fluxes. Unfortunately, blowing snow processes are not presently incorporated in any hydrological or meteorological models. To demonstrate the application of simple algorithms that represent blowing snow processes, monthly snow accumulation, relocation and sublimation fluxes were calculated and applied in a spatially distributed manner to a 68-km2 catchment in the low Arctic of north-western Canada. The model uses a Landsat-derived vegetation classification and a digital elevation model to segregate the basin into snow ‘sources’ and ‘sinks’. The model then relocates snow from sources to sinks and calculates in-transit sublimation loss. The resulting annual snow accumulation in specific landscape types was compared with the result of intensive surveys of snow depth and density. On an annual basis, 28% of annual snowfall sublimated from tundra surfaces whilst 18% was transported to sink areas. Annual blowing snow transport to sink areas amounted to an additional 16% of annual snowfall to shrub–tundra and an additional 182% to drifts. For the catchment, 19·5% of annual snowfall sublimated from blowing snow, 5·8% was transported into the catchment and 86·5% accumulated on the ground. The model overestimated snow accumulation in the catchment by 6%. The application demonstrates that winter precipitation alone is insufficient to calculate snow accumulation and that blowing snow processes and landscape patterns govern the spatial distribution and total accumulation of snow water equivalent over the winter. These processes can be modelled by relatively simple algorithms, and, when distributed by landscape type over the catchment, produce reasonable estimates of snow accumulation and loss in wind-swept regions. © 1997 John Wiley & Sons, Ltd.  相似文献   

15.
We analyse spatial variability and different evolution patterns of snowpack in a mixed beech–fir stand in the central Pyrenees. Snow depth and density were surveyed weekly along six transects of contrasting forest cover during a complete accumulation and melting season; we also surveyed a sector unaffected by canopy cover. Forest density was measured using the sky view factor (SVF) obtained from digital hemispherical photographs. During periods of snow accumulation and melting, noticeable differences in snow depth and density were found between the open site and those areas covered by forest canopy. Principal component analysis provided valuable information in explaining these observations. The results indicate a high variability in snow accumulation within forest areas related to differences in canopy density. Maximum snow water equivalent (SWE) was reduced by more than 50% beneath dense canopies compared with clearings, and this difference increased during the melting period. We also found significant temporal variations: when melting began in sectors with low SVF, most of the snow had already thawed in areas with high SVF. However, specific conditions occasionally produced a different response of SWE to forest cover, with lower melting rates observed beneath dense canopies. The high values of correlation coefficients for SWE and SVF (r > 0·9) indicate the reliability of predicting the spatial distribution of SWE in forests when only a moderate number of observations are available. Digital hemispherical photographs provide an appropriate tool for this type of analysis, especially for zenith angles in the range 35–55 . Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

16.
Abstract

We simulated snow processes in a forested region with heavy snowfall in Japan, and evaluated both the regional-scale snow distribution and the potential impact of land-use changes on the snow cover and water balances over the entire domain. SnowModel reproduced the snow processes at open and forested sites, which were confirmed by snow water equivalent (SWE) measurements at two intensive observation sites and snow depth measurements at the Automated Meteorological Data Acquisition System sites. SnowModel also reproduced the observed snow distribution (from the MODIS snow cover data) over the simulation domain during thaw. The observed SWE was less at the forested site than at the open site. The SnowModel simulations showed that this difference was caused mainly by differences in sublimation. The type of land use changed the maximum SWE, onset and duration of snowmelt, and the daily snowmelt rate due to canopy snow interception.

Citation Suzuki, K., Kodama, Y., Nakai, T., Liston, G. E., Yamamoto, K., Ohata, T., Ishii, Y., Sumida, A., Hara, T. & Ohta, T. (2011) Impact of land-use changes in a forested region with heavy snowfall in Hokkaido, Japan. Hydrol. Sci. J. 56(3), 443–467.  相似文献   

17.
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   

18.
The snowcover energy balance is typically dominated by net radiation and sensible and latent heat fluxes. Validation of the two latter components is rare and often difficult to undertake at complex mountain sites. Latent heat flux, the focus of this paper, is the primary coupling mechanism between the snow surface and the atmosphere. It accounts for the critical exchange of mass (sublimation or condensation), along with the associated snowcover energy loss or gain. Measured and modelled latent heat fluxes at a wind‐exposed and wind‐sheltered site were compared to evaluate variability in model parameters. A well‐tested and well‐validated snowcover energy balance model, Snobal, was selected for this comparison because of previously successful applications of the model at these sites and because of the adjustability of the parameters specific to latent heat transfer within the model. Simulated latent heat flux and snow water equivalent (SWE) were not sensitive to different formulations of the stability profile functions associated with heat transfer calculations. The model parameters of snow surface roughness length and active snow layer thickness were used to improve latent heat flux simulations while retaining accuracy in the simulation of the SWE at an exposed and sheltered study site. Optimal parameters for simulated latent heat flux and SWE were found at the exposed site with a shorter roughness length and thicker active layer, and at the sheltered site with a longer roughness length and thinner active layer. These findings were linked to physical characteristics of the study sites and will allow for adoption into other snow models that use similar parameters. Physical characteristics of wind exposure and cover could also be used to distribute critical parameters in a spatially distributed modelling domain and aid in parameter selection for application to other watersheds where detailed information is not available. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

19.
In snow-fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km2 forested catchment in southern Québec, Canada (47°N, 71°W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high-resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index [LAI], tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small-scale variability across the catchment and 1810 manual snow samples were collected throughout the consecutive winters of 2016–17 and 2017–18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the LAI. We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression, binary regression tree and neural networks (NN). We were unable to establish any relationship between the CV of SWE and the LAI. However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (Nash-Sutcliffe efficiency [NSE] = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE. Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66).  相似文献   

20.
We report a methodology for reconstructing the daily snow depth distribution at high spatial resolution in a small Pyrenean catchment using time‐lapse photographs and snow depletion rates derived from an on‐site measuring meteorological station. The results were compared with the observed snow depth distribution, determined on a number of separate occasions using a terrestrial laser scanner (TLS). The time‐lapse photographs were projected onto a digital elevation model of the study site, and converted into snow presence/absence information. The melt‐out date (MOD; first occurrence of melt out after peak snow accumulation) was obtained from the projected photograph series. Commencing the backward reconstruction for each grid cell at the MOD, the method uses simulated snow depth depletion rates using a temperature index approach, which are extrapolated to the grid cells of the domain to arrive at the snow distribution of the previous day. Two variants of the reconstruction techniques were applied (1) using a spatially constant degree day factor (DDF) for calculating the daily expected snow depth depletion rate, and (2) allowing a spatially distributed DDF calculated from two consecutive TLS acquisitions compared to the snow depth depletion rate observed at the meteorological station. Validation revealed that both methods performed well (average R2 = 0.68; standard RMSE = 0.58), with better results obtained from the spatially distributed approach. Nevertheless, the spatially corrected DDF reconstruction, which requires TLS data, suggests that the constant DDF approach is an efficient, and for most applications sufficiently accurate and easily reproducible method. The results highlight the usefulness of time‐lapse photography for not only determining the snow covered area, but also for estimating the spatial distribution of snow depth. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

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