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1.
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   

2.
ABSTRACT

This study investigates changes in seasonal runoff and low flows related to changes in snow and climate variables in mountainous catchments in Central Europe. The period 1966–2012 was used to assess trends in climate and streamflow characteristics using a modified Mann–Kendall test. Droughts were classified into nine classes according to key snow and climate drivers. The results showed an increase in air temperature, decrease in snowfall fraction and snow depth, and changes in precipitation. This resulted in increased winter runoff and decreased late spring runoff due to earlier snowmelt, especially at elevations from 1000 to 1500 m a.s.l. Most of the hydrological droughts were connected to either low air temperatures and precipitation during winter or high winter air temperatures which caused below-average snow storages. Our findings show that, besides precipitation and air temperature, snow plays an important role in summer streamflow and drought occurrence in selected mountainous catchments.  相似文献   

3.
Warm winters and high precipitation in north-eastern Japan generate snow covers of more than three meters depth and densities of up to 0.55 g cm−3. Under these conditions, rain/snow ratio and snowmelt have increased significantly in the last decade under increasing warm winters. This study aims at understanding the effect of rain-on-snow and snowmelt on soil moisture under thick snow covers in mid-winter, taking into account that snowmelt in spring is an important source of water for forests and agriculture. The study combines three components of the Hydrosphere (precipitation, snow cover and soil moisture) in order to trace water mobility in winter, since soil temperatures remained positive in winter at nearly 0.3°C. The results showed that soil moisture increased after snowmelt and especially after rain-on-snow events in mid-winter 2018/2019. Rain-on-snow events were firstly buffered by fresh snow, increasing the snow water equivalent (SWE), followed by water soil infiltration once the water storage capacity of the snowpack was reached. The largest increase of soil moisture was 2.35 vol%. Early snowmelt increased soil moisture with rates between 0.02 and 0.035 vol% hr−1 while, rain-on-snow events infiltrated snow and soil faster than snowmelt and resulted in rates of up to 1.06 vol% hr−1. These results showed the strong connection of rain, snow and soil in winter and introduce possible hydrological scenarios in the forest ecosystems of the heavy snowfall regions of north-eastern Japan. Effects of rain-on-snow events and snowmelt on soil moisture were estimated for the period 2012–2018. Rain/snow ratio showed that only 30% of the total precipitation in the winter season 2011/2012 was rain events while it was 50% for the winter 2018/2019. Increasing climate warming and weakening of the Siberian winter monsoons will probably increase rain/snow ratio and the number of rain-on-snow events in the near future.  相似文献   

4.
Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (TB) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

5.
Water losses from snow intercepted by forest canopy can significantly influence the hydrological cycle in seasonally snow‐covered regions, yet how snow interception losses (SIL) are influenced by a changing climate are poorly understood. In this study, we used a unique 30 year record (1986–2015) of snow accumulation and snow water equivalent measurements in a mature mixed coniferous (Picea abies and Pinus sylvestris ) forest stand and an adjacent open area to assess how changes in weather conditions influence SIL. Given little change in canopy cover during this study, the 20% increase in SIL was likely the result of changes in winter weather conditions. However, there was no significant change in average wintertime precipitation and temperature during the study period. Instead, mean monthly temperature values increased during the early winter months (i.e., November and December), whereas there was a significant decrease in precipitation in March. We also assessed how daily variation in meteorological variables influenced SIL and found that about 50% of the variation in SIL was correlated to the amount of precipitation that occurred when temperatures were lower than ?3 °C and to the proportion of days with mean daily temperatures higher than +0.4 °C. Taken together, this study highlights the importance of understanding the appropriate time scale and thresholds in which weather conditions influence SIL in order to better predict how projected climate change will influence snow accumulation and hydrology in boreal forests in the future.  相似文献   

6.
A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.  相似文献   

7.
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   

8.
The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

9.
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10.
A theory of pressure sensor response in snow is derived and used to examine the sources of measurement errors in snow water equivalent (SWE) pressure sensors. Measurement errors in SWE are caused by differences in the compressibility of the pressure sensor and the adjacent snow layer, which produces a shear stress along the perimeter of the sensor. When the temperature at the base of the snow cover equals 0 °C, differences in the snowmelt rate between the snow–SWE sensor interface and the adjacent snow–soil interface may also produce a shear stress along the sensor's perimeter. This shear stress perturbs the pressure field over the sensor, producing SWE measurement errors. Snow creep acts to reduce shear stresses along the SWE sensor's perimeter at a rate that is inversely proportional to the snow viscosity. For sustained periods of differential snowmelt, a difference in the mass of snow over the sensor compared with the surrounding soil will develop, producing additional permanent errors in SWE measurements. The theory indicates that SWE pressure sensor performance can be improved by designing a sensor with a high Young's modulus (low compressibility), low aspect ratio, large diameter and thermal properties that match those of the surrounding soil. Simulations of SWE pressure sensor errors using the theory are in close agreement with observed errors and may provide a means to correct historical SWE measurements for use in hydrological hindcast or climate studies. Published in 2003 by John Wiley & Sons, Ltd.  相似文献   

11.
We investigated trends in future seasonal runoff components in the Willamette River Basin (WRB) of Oregon for the twenty‐first century. Statistically downscaled climate projections by Climate Impacts Group (CIG), eight different global climate model (GCM) simulations with two different greenhouse gas (GHG) emission scenarios, (A1B and B1), were used as inputs for the US Geological Survey's Precipitation Runoff Modelling System. Ensemble mean results show negative trends in spring (March, April and May) and summer (June, July and August) runoff and positive trends in fall (September, October and November) and winter (December, January and February) runoff for 2000–2099. This is a result of temperature controls on the snowpack and declining summer and increasing winter precipitation. With temperature increases throughout the basin, snow water equivalent (SWE) is projected to decline consistently for all seasons. The decreases in the centre of timing and 7‐day low flows and increases in the top 5% flow are caused by the earlier snowmelt in spring, decreases in summer runoff and increases in fall and winter runoff, respectively. Winter runoff changes are more pronounced in higher elevations than in low elevations in winter. Seasonal runoff trends are associated with the complex interactions of climatic and topographic variables. While SWE is the most important explanatory variable for spring and winter runoff trends, precipitation has the strongest influence on fall runoff. Spatial error regression models that incorporate spatial dependence better explain the variations of runoff trends than ordinary least‐squares (OLS) multiple regression models. Our results show that long‐term trends of water balance components in the WRB could be highly affected by anthropogenic climate change, but the direction and magnitude of such changes are highly dependent on the interactions between climate change and land surface hydrology. This suggests a need for spatially explicit adaptive water resource management within the WRB under climate change. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

12.
Seasonal low flows are important for sustaining ecosystems and for supplying human needs during the dry season. In California's Sierra Nevada mountains, low flows are primarily sustained by groundwater that is recharged during snowmelt. As the climate warms over the next century, the volume of the annual Sierra Nevada snowpack is expected to decrease by ~40–90%. In eight snow‐dominated catchments in the Sierra Nevada, we analysed records of snow water equivalent (SWE) and unimpaired streamflow records spanning 10–33 years. Linear extrapolations of historical SWE/streamflow relationships suggest that annual minimum flows in some catchments could decrease to zero if peak SWE is reduced to roughly half of its historical average. For every 10% decrease in peak SWE, annual minimum flows decrease 9–22% and occur 3–7 days earlier in the year. In two of the study catchments, Sagehen and Pitman Creeks, seasonal low flows are significantly correlated with the previous year's snowpack as well as the current year's snowpack. We explore how future warming could affect the relationship between winter snowpacks and summer low flows, using a distributed hydrologic model Regional Hydro‐ecologic Ecosystem Simulation System (RHESSys) to simulate the response of two study catchments. Model results suggest that a 10% decrease in peak SWE will lead to a 1–8% decrease in low flows. The modelled streams do not dry up completely, because the effects of reduced SWE are partly offset by increased fall or winter net gains in storage, and by shifts in the timing of peak evapotranspiration. We consider how groundwater storage, snowmelt and evapotranspiration rates, and precipitation phase (snow vs rain) influence catchment response to warming. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

13.
Long‐term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long‐term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere‐ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze–thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow‐covered days. This shortening of the frost‐covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil. Published in 2010 by John Wiley & Sons, Ltd.  相似文献   

14.
Snow accumulation and ablation rule the temporal dynamics of water availability in mountain areas and cold regions. In these environments, the evaluation of the snow water amount is a key issue. The spatial distribution of snow water equivalent (SWE) over a mountain basin at the end of the snow accumulation season is estimated using a minimal statistical model (SWE‐SEM). This uses systematic observations such as ground measurements collected at snow gauges and snow‐covered area (SCA) data retrieved by remote sensors, here MODIS. Firstly, SWE‐SEM calculates local SWE estimates at snow gauges, then the spatial distribution of SWE over a certain area using an interpolation method; linear regressions of the first two order moments of SWE with altitude. The interpolation has been made by both confining and unconfining the spatial domain by SCA. SWE‐SEM is applied to the Mallero basin (northern Italy) for calculating the snow water equivalent at the end of the winter season for 6 years (2001–2007). For 2007, SWE‐SEM estimates are validated through fieldwork measurements collected during an ‘ad hoc’ campaign on March 31, 2007. Snow‐surveyed measurements are used to check SCA, snow density and SWE estimates. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

15.
Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

16.
The temporal and spatial continuity of spatially distributed estimates of snow‐covered area (SCA) are limited by the availability of cloud‐free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt–Verde watershed of Arizona. Gridded positive accumulated degree‐days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

17.
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   

18.
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   

19.
In the western USA, shifts from snow to rain precipitation regimes and increases in western juniper cover in shrub‐dominated landscapes can alter surface water input via changes in snowmelt and throughfall. To better understand how shifts in both precipitation and semi‐arid vegetation cover alter above‐ground hydrological processes, we assessed how rain interception differs between snow and rain surface water input; how western juniper alters snowpack dynamics; and how these above‐ground processes differ across western juniper, mountain big sagebrush and low sagebrush plant communities. We collected continuous surface water input with four large lysimeters, interspace and below‐canopy snow depth data and conducted periodic snow surveys for two consecutive water years (2013 and 2014). The ratio of interspace to below‐canopy surface water input was greater for snow relative to rain events, averaging 79.4% and 54.8%, respectively. The greater surface water input ratio for snow is in part due to increased deposition of redistributed snow under the canopy. We simulated above‐ground energy and water fluxes in western juniper, low sagebrush and mountain big sagebrush for two 8‐year periods under current and projected mid‐21st century warmer temperatures with the Simultaneous Heat and Water (SHAW) model. Juniper compared with low and mountain sagebrush reduced surface water input by an average of 138 mm or 24% of the total site water budget. Conversely, warming temperatures reduced surface water input by only an average of 14 mm across the three vegetation types. The future (warmer) simulations resulted in earlier snow disappearance and surface water input by 51 and 45 days, respectively, across juniper, low sagebrush and mountain sagebrush. Information from this study can help land managers in the sagebrush steppe understand how both shifts in climate and semi‐arid vegetation will alter fundamental hydrological processes. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

20.
Leaf area index (LAI) and canopy coverage are important parameters when modelling snow process in coniferous forests, controlling interception and transmitting radiation. Estimates of LAI and sky view factor show large variability depending on the estimation method used, and it is not clear how this is reflected in the calculated snow processes beneath the canopy. In this study, the winter LAI and sky view fraction were estimated using different optical and biomass‐based approximations in several boreal coniferous forest stands in Fennoscandia with different stand density, age and site latitude. The biomass‐based estimate of LAI derived from forest inventory data was close to the values derived from the optical measurements at most sites, suggesting that forest inventory data can be used as input to snow hydrological modelling. Heterogeneity of tree species and site fertility, as well as edge effects between different forest compartments, caused differences in the LAI estimates at some sites. A snow energy and mass balance model (SNOWPACK) was applied to detect how the differences in the estimated values of the winter LAI and sky view fraction were reflected in simulated snow processes. In the simulations, an increase in LAI and a decrease in sky view fraction changed the snow surface energy balance by decreasing shortwave radiation input and increasing longwave radiation input. Changes in LAI and sky view fraction affected directly snow accumulation through altered throughfall fraction and indirectly snowmelt through the changed surface energy balance. Changes in LAI and sky view fraction had a greater impact on mean incoming radiation beneath the canopy than on other energy fluxes. Snowmelt was affected more than snow accumulation. The effect of canopy parameters on evaporation loss from intercepted snow was comparable with the effect of variation in governing meteorological variables such as precipitation intensity and air temperature. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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