Vegetation structure controls on snow and soil moisture in restored ponderosa pine forests

Thinning of semi-arid forests to reduce wildfire risk is believed to improve forest health by increasing soil moisture. Increased snowpack, reduced transpiration and reduced rainfall interception are frequently cited mechanisms by which reduced canopy density may increase soil moisture. However, the relative importance of these factors has not been rigorously evaluated in field studies. We measured snow depth, snow water equivalent (SWE) and the spatial and temporal variation in soil moisture at four experimental paired treatment-control thinning sites in high elevation ponderosa pine forest northern Arizona, USA. We compared snow and soil moisture measurements with forest structure metrics derived from aerial imagery and 3-dimensional lidar data to determine the relationship between vegetation structure, snow and soil moisture throughout the annual hydrologic cycle. Soil moisture was consistently and significantly higher in thinned forest plots, even though the treatments were performed 8–11 years before this study. However, we did not find evidence that SWE was higher in thinned forests across a range of snow conditions. Regression tree analysis of soil moisture and vegetation structure data provided some evidence that localized differences in transpiration and interception of precipitation influence the spatial pattern of soil moisture at points in the annual hydrologic cycle when the system is becoming increasingly water limited. However, vegetation structure explained a relatively low amount of the spatial variance (R² < 0.23) in soil moisture. Continuous measurements of soil moisture in depth profiles showed stronger attenuation of soil moisture peaks in thinned sites, suggesting differences in infiltration dynamics may explain the difference in soil moisture between treatments as opposed to overlying vegetation alone. Our results show limited support for commonly cited relationships between vegetation structure, snow and soil moisture and indicate that future research is needed to understand how reduction in tree density alters soil hydraulic properties.     

Long‐term investigations of the snow cover in a subalpine semi‐forested catchment

To improve spring runoff forecasts from subalpine catchments, detailed spatial simulations of the snow cover in this landscape is obligatory. For more than 30 years, the Swiss Federal Research Institute WSL has been conducting extensive snow cover observations in the subalpine watershed Alptal (central Switzerland). This paper summarizes the conclusions from past snow studies in the Alptal valley and presents an analysis of 14 snow courses located at different exposures and altitudes, partly in open areas and partly in forest. The long‐term performance of a physically based numerical snow–vegetation–atmosphere model (COUP) was tested with these snow‐course measurements. One single parameter set with meteorological input variables corrected to the prevailing local conditions resulted in a convincing snow water equivalent (SWE) simulation at most sites and for various winters with a wide range of snow conditions. The snow interception approach used in this study was able to explain the forest effect on the SWE as observed on paired snow courses. Finally, we demonstrated for a meadow and a forest site that a successful simulation of the snowpack yields appropriate melt rates. Copyright © 2006 John Wiley & Sons, Ltd.     

EFFECTS OF FOREST ON THE SNOW PARAMETERS DERIVED FROM MICROWAVE MEASUREMENTS DURING THE BOREAS WINTER FIELD CAMPAIGN

Passive microwave data have been used to infer the areal snow water equivalent (SWE) with some success. However, the accuracy of these retrieved SWE values have not been well determined for heterogeneous vegetated regions. The Boreal Ecosystem–Atmosphere Study (BOREAS) Winter Field Campaign (WFC), which took place in February 1994, provided the opportunity to study in detail the effects of boreal forests on snow parameter retrievals. Preliminary results reconfirmed the relationship between microwave brightness temperature and snow water equivalent. The pronounced effect of forest cover on SWE retrieval was studied. A modified vegetation mixing algorithm is proposed to account for the forest cover. The relationship between the microwave signature and observed snowpack parameters matches results from this model.     

Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkaido,Japan

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.     

Performance characteristics of a new electronic snow water equivalent sensor in different climates

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.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

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.     

Potential of time‐lapse photography of snow for hydrological purposes at the small catchment scale

Time‐lapse photography provides an attractive source of information about snow cover characteristics, especially at the small catchment scale. The objective of this study was to design and test a monitoring system, which allows multi‐resolution observations of snow cover characteristics. The main aim was to simultaneously investigate the spatio‐temporal patterns of snow cover, snow depth and snowfall interception in the area very close to the camera, and the spatio‐temporal patterns of snow cover in the far range. The multi‐resolution design was tested at three sites in the eastern part of the Austrian Alps (Hochschwab‐Rax region). Digital photographs were taken at hourly time steps between 6:00 and 18:00 in the period November, 2004 to December, 2006. The results showed that the time‐lapse photography allows effective mapping of the snow depths at high temporal resolution in the region close to the digital camera at many snow stake locations. It is possible to process a large number of photos by using an automatic procedure for accurate snow depth readings. The digital photographs can also be used to infer the settling characteristics of the snow pack and snow interception during the day. Although it is not possible to directly estimate the snow interception mass, the photos may indeed give very useful information on the snow processes on and beneath the forest canopy. The main advantage of using time‐lapse photography in the far range of the digital camera is to observe the spatio‐temporal patterns of snow cover over different landscape configurations. The results illustrate that digital photographs can be very useful for parameterising processes such as sloughing on steep slopes, snow deposition in gullies and snow erosion on mountain ridges in a distributed snow model. Copyright © 2011 John Wiley & Sons, Ltd.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

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.     

Charred forests accelerate snow albedo decay: parameterizing the post‐fire radiative forcing on snow for three years following fire

As large, high‐severity forest fires increase and snowpacks become more vulnerable to climate change across the western USA, it is important to understand post‐fire disturbance impacts on snow hydrology. Here, we examine, quantify, parameterize, model, and assess the post‐fire radiative forcing effects on snow to improve hydrologic modelling of snow‐dominated watersheds having experienced severe forest fires. Following a 2011 high‐severity forest fire in the Oregon Cascades, we measured snow albedo, monitored snow, and micrometeorological conditions, sampled snow surface debris, and modelled snowpack energy and mass balance in adjacent burned forest (BF) and unburned forest sites. For three winters following the fire, charred debris in the BF reduced snow albedo, accelerated snow albedo decay, and increased snowmelt rates thereby advancing the date of snow disappearance compared with the unburned forest. We demonstrate a new parameterization of post‐fire snow albedo as a function of days‐since‐snowfall and net snowpack energy balance using an empirically based exponential decay function. Incorporating our new post‐fire snow albedo decay parameterization in a spatially distributed energy and mass balance snow model, we show significantly improved predictions of snow cover duration and spatial variability of snow water equivalent across the BF, particularly during the late snowmelt period. Field measurements, snow model results, and remote sensing data demonstrate that charred forests increase the radiative forcing to snow and advance the timing of snow disappearance for several years following fire. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling snow water equivalent and spring runoff in a boreal watershed,James Bay,Canada

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.     

Assimilating snow observations to snow interception process simulations

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.     

Routing of canopy drip in the snowpack below a spruce crown

Snow that is retained by a forest canopy may either sublimate or evaporate directly from the crown or drop as snow clumps or meltwater to the ground. Redistributed snow and meltwater affect the snow structure and prevent the formation of mechanically weak layers, which is the prerequisite for avalanche formation in forests. In this paper we describe the results of dye tracer experiments conducted in a subalpine forest near Davos, Switzerland. Before a snowfall event we stained snow‐free branches of a spruce with a dye tracer solution. After snowfall the coloured meltwater dripping from the branches down on to the snowpack stained the percolation pathways of the meltwater in the snowpack. Photographs of the snow profiles indicate that the meltwater seeped almost vertically through the isothermal snowpack to the soil surface not exceeding the projected crown edge. Meltwater of different events moves along different preferential flow channels in the snow suggesting that old channels are not non‐conducting and additional meltwater fronts create new channels. Copyright © 1999 John Wiley & Sons, Ltd.     

Estimating snow distribution over a large area and its application for water resources

The spatial and temporal distribution of the snow water equivalent (SWE), snow density and snow depth were estimated by a method combining remote sensing technology and degree‐day techniques over a study area of 370 000 km². The advantages of this simulation model are its simplicity and the availability of degree‐day parameters, which can be successively evaluated by referring to snow area maps created from satellite images. This simulation worked very well for estimating SWE and helped to separate the areas of thin snow cover from heavier snowfall. However, shallow snow in warm regions led to some misjudgments in the snow area maps because of the time lag between when the satellite image was acquired and the simulation itself. Vulnerable areas, where a large variation in the amount of snow affects people's life, could be identified from the differences between heavy and light snow years. This vulnerability stems from a predicted lack of irrigation water for rice production caused by future climate change. The model developed in this study has the potential to contribute to water management activities and decision‐making processes when considering necessary adaptations to future climate change. Copyright © 2007 John Wiley & Sons, Ltd.     

The implications of permafrost thaw and land cover change on snow water equivalent accumulation,melt and runoff in discontinuous permafrost peatlands

In the discontinuous permafrost zone of the Northwest Territories (NWT), Canada, snow covers the ground surface for half the year. Snowmelt constitutes a primary source of moisture supply for the short growing season and strongly influences stream hydrographs. Permafrost thaw has changed the landscape by increasing the proportional coverage of permafrost-free wetlands at the expense of permafrost-cored peat plateau forests. The biophysical characteristics of each feature affect snow water equivalent (SWE) accumulation and melt rates. In headwater streams in the southern Dehcho region of the NWT, snowmelt runoff has significantly increased over the past 50 years, despite no significant change in annual SWE. At the Fort Simpson A climate station, we found that SWE measurements made by Environment and Climate Change Canada using a Nipher precipitation gauge were more accurate than the Adjusted and Homogenized Canadian Climate Dataset which was derived from snow depth measurements. Here, we: (a) provide 13 years of snow survey data to demonstrate differences in end-of-season SWE between wetlands and plateau forests; (b) provide ablation stake and radiation measurements to document differences in snow melt patterns among wetlands, plateau forests, and upland forests; and (c) evaluate the potential impact of permafrost-thaw induced wetland expansion on SWE accumulation, melt, and runoff. We found that plateaus retain significantly (p < 0.01) more SWE than wetlands. However, the differences are too small (123 mm and 111 mm, respectively) to cause any substantial change in basin SWE. During the snowmelt period in 2015, wetlands were the first feature to become snow-free in mid-April, followed by plateau forests (7 days after wetlands) and upland forests (18 days after wetlands). A transition to a higher percentage cover of wetlands may lead to more rapid snowmelt and provide a more hydrologically-connected landscape, a plausible mechanism driving the observed increase in spring freshet runoff.     

Snow water equivalent estimation in the Mallero basin using snow gauge data and MODIS images and fieldwork validation

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.     

Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario,Canada

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.     

The simulation of heat and water exchange in the boreal spruce forest by the land-surface model SWAP

All previous versions of a physically based land-surface model SWAP have assumed for simplicity that vegetation is fully covered by snow during the cold season. Such assumption is reasonable only for the regions dominated by short vegetation or for warm climates where snow processes are absent. The major goals of this paper are (i) modification of the latest version of SWAP by incorporation of tall vegetation into the cold-season parameterizations to make the model applicable for simulating heat and water transfer within a boreal forest biome and (ii) validation of the modified version using the data from a forested catchment located in the boreal zone. Modification of SWAP required to parameterize radiative and turbulent exchange between the forest crown and forest floor, partitioning snowfall between interception by the canopy (in doing so, snow interception differs from rain interception) and falling to the ground, formation of snow cover on the forest crown and forest floor including snow accumulation (both in solid and liquid fractions), snow evaporation, and snowmelt. The advanced model was validated using a set of hydrometeorological data measured during 18 years (1966–1983) at the Tayozhniy catchment (covered by boreal spruce forest), Valdai, Russia. Simulations of annual and monthly snow/rain interception, daily runoff at the catchment outlet, snow density, snow depth, snow water equivalent, soil water storage in three layers (0–20, 0–50 and 0–100 cm), and monthly evapotranspiration from the catchment were compared with observations. Analysis of the results of validation shows that the new version of the model SWAP reproduces the heat and water exchange processes occurring in mid-latitude boreal forest quite reasonable.     

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

Observations of late winter Canadian tundra snow cover properties

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.     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.