Climate change effects on the snowmelt hydrology of western NorthAmerican mountain basins

Several effects of increasing atmospheric CO₂ content on climate are considered-namely, increasing atmospheric temperature, changing snowmelt equivalent at the beginning of the snowmelt, and changing precipitation during the snowmelt runoff season. The effects that these changes would have on runoff from mountain basins were evaluated by use of the snowmelt runoff model (SRM). The major effect on runoff was caused by increasing temperature. An increase in total seasonal runoff resulted, but the more interesting and significant effect was a redistribution of runoff to the months April and May. Depending on whether snowmelt season precipitation and winter snow accumulation increased or decreased, the temperature effect on the hydrograph was magnified or diminished. The model results indicated a potentially serious problem in western North America where climate changes could widen the gap between water supply and water demand as well as causing existing water storage and distribution systems to be ineffective     

The date of snow disappearance on the Arctic tundra as determinedfrom satellite, meteorological station and radiometric in situobservations

Satellite-derived snow cover maps for sites in Alaska, Canada, Scandinavia and Siberia were employed to assess the date when snow disappeared on the Arctic tundra and to determine whether the snow has been melting earlier in the spring in recent years. Results show that for three of the four sites there has been a tendency toward earlier snowmelt during the 1980s. In Alaska, the satellite-derived date of snowmelt was compared to the date of snowmelt as observed at the Barrow meteorological station and a site near Barrow where radiometric in situ measurements were made for the last 5 years. The three data sources complement each other even though the satellite site is located 150 km from Barrow. One mechanism which could cause a trend toward earlier snowmelt in Alaska is the deposition of soot and particulates on the snow surface as a result of Arctic haze     

Runoff Modeling from Snow Covered Area

Snowmelt-runoff models with a deterministic approach require measurements of the snow covered area provided by remote sensing. An index of the snow accumulation in a given winter reduces the ambiguity of the snow coverage when used for runoff volume forecasts and enables the depletion curves of snow covered areas to be extrapolated for operational short-term discharge forecasts.     

Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007

Determining the date of snowmelt clearance is an important issue for hydrological and climate research. Spaceborne radiometers are ideally suited for global snowmelt monitoring. In this paper, four different algorithms are used to determine the snowmelt date from Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager data for a nearly 30-year period. Algorithms are based on thresholding channel differences, on applying neural networks, and on time series analysis. The results are compared with ground-based observations of snow depth and snowmelt status available through the Russian INTAS-SSCONE observation database. Analysis based on Moderate Resolution Imaging Spectroradiometer data indicates that these pointwise observations are applicable as reference data. The obtained error estimates indicate that the algorithm based on time series analysis has the highest performance. Using this algorithm, a time series of the snowmelt from 1979 to 2007 is calculated for the whole Eurasia showing a trend of an earlier snow clearance. The trend is statistically significant. The results agree with earlier research. The novelty here is the demonstration and validation of estimates for a large continental scale (for areas dominated by boreal forests) using extensive reference data sets.      

基于GF-6 PMS影像的积雪信息识别

以黑龙江省哈尔滨市道外区为研究区,系统探讨分析了基于遥感的不同方法在积雪信息识别中的应用.首先,对研究区两个时相的高分六号(GF-6)多光谱相机(PMS)影像进行目视解译,掌握了研究区内地物类型和积雪分布特点.其次,基于目视解译结果,选取了8种典型地物类型,得到了"积雪"和"非雪"两类像元的光谱特征规律.再次,探讨分析...     

Snow mapping in alpine regions with synthetic aperture radar

Active microwave sensors can discriminate snow from other surfaces in all weather conditions, and their spatial resolution is compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR in the Otztal Alps in 1989 and 1991, the authors examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. By comparing polarimetric SAR data to images from the Landsat Thematic Mapper obtained under clear conditions one week after the SAR flight, the authors found that SAR data at 5.3 GHz (C-band) can discriminate between areas covered by snow from those that are ice-free. However, they are less suited to discrimination of glacier ice from snow and rock. The overall pixel-by-pixel accuracies-74% from VV polarization alone with topographic information, 76% from polarimetric SAR without any topographic information, and 79% from polarimetric SAR with topographic information-are high enough to justify the use of SAR as the data source in areas that are too cloud-covered to obtain data from the Thematic Mapper. This is especially true for snow discrimination, where accuracies exceed 80%, because mapping of a transient snow cover during a cloudy melt season is often difficult with an optical sensor. The AIRSAR survey was carried out in summer during a heavy rainstorm, when the snow surfaces were unusually rough. Even better results for snow discrimination can be expected for mapping in the spring, when snow is usually smoother     

Inferring snow wetness using C-band data from SIR-C's polarimetricsynthetic aperture radar

In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25°-70° and for surface roughness with rms height ⩽7 mm and correlation length ⩽25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs     

基于红外光反射的雪深检测传感器研究与应用

冬季河流湖泊冰盖表面的积雪深度监测是水文信息化领域的一项切实需求,基于空气、积雪和冰盖三种介质对红外光的传输特性差异,设计了一种可以连续实时对积雪深度进行定点检测的传感器。传感器及检测系统于2016年12月初在我国黑龙江上游漠河县江段进行了现场安装与实验测试,通过与传统对射式雪深检测传感器以及当地水文站人工测试结果对比,基于红外光反射特性的雪深检测传感器灵敏度更高,能更准确反映出积雪深度的变化过程,且具有体积小,布设安装方便等特点,为高寒地区野外恶劣环境下积雪深度的定点高精度检测提供了一种新的解决方法和技术装备。     

Snow-Cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data

The Nimbus-7 satellite launched on October 24, 1978, carries a multifrequency, dual-polarized microwave imager. The instrument is designed to sense the ocean surface, the atmosphere, and land surfaces remotely. From previous ground-based and satellite-based microwave experiments, it is well known, that snow cover over land has a very distinct effect on the microwave signatures of the earth surface. It was the goal of this study to show that the three snow-cover parameters: extent, snow water equivalent, and onset of snow melt can be determined using scanning multichannel microwave radiometer (SMMR) data. Our analysis has shown, that the three snow parameters mentioned above are retrievable with sufficient accuracy to be of great value in climatology, meteorology, and hydrology. Snow extent is determined for dry snow cover with depth ?5 cm, snow water equivalent can be determined on a regional basis with ?2 g/cm2 rms accuracy, and the onset of snow melt is clearly visible by the detection of melt and refreeze cycles prior to snow runoff. The algorithms derived are simple enough to be incorporated in fully automated operational data analysis schemes.     

Towards the Definition of Optimum Sensor Specifications for Microwave Remote Sensing of Snow

The interaction of nicrowaves with snow strongly depends on parameters such as snow wetness and the size and structure of snow grains. Therefore microwave radiometry and scatterometry are excellent tools for remote sensing of the snowcover. Multifrequency radiometry can be used to classify snow as was shown with ground-based measurements of the period April-June 1977 at a high altitude Alpine test site. The continuation of the measurement program yielded data of 3 additional snow seasons with widely varying snow conditions, therefore the present information has become representative for alpine regions. Relationships between the brightness temperature and the water equivalents show a similar variation with snow type as in other snow regions, so that the range of validity of our data set is not restricted to the Alps. The problem of discriminating regions of wet snow from snow-free land is found to be solvable with microwave scatterometry. Two cluster analyses in factorial spaces of both the ground truth and the microwave data sets demonstrate the potential of microwave sensors to classify snow which is a prerequisite for snow algorithms retrieving hydrologic parameters. The results are used to define sensor specifications with optimum sensitivity for microwave remote sensing of snow.     

Distributed Energy Efficient Tracking in Hybrid wireless sensor network (DEETH)

In wireless sensor network (WSN), it is a complex task to track the target when it is moving randomly in an unknown environment. It also becomes difficult to cover a complete searching area because of the limited searching range and energy of sensor nodes as they are few in number. The author proposes a distributed energy efficient tracking in a hybrid WSN (DEETH) to track a randomly moving target in an unknown searching. Hybrid WSN that is proposed has both static sensor nodes (SSNs) and mobile sensor nodes (MSNs), which are deployed in the searching area. The MSNs move collectively using particle swarm techniques to search a target. The SSNs are deployed for tracking the presence of a target and giving this information to the base station. As per the information given by SSN, MSNs travel to the target and track it. Simulation results prove that proposed technique successfully tracks the target using less number of nodes and also less amount of energy.     

Snow Fork for Field Determination of the Density and Wetness Profiles of a Snow Pack

A radiowave sensor (a snow fork) for determining the density and wetness profiles of a snow pack with a single measurement has been developed. The snow fork is based on the measurement of the dielectric properties (real and imaginary part) of snow around 1 GHz. Due to the open structure of the resonator the measurement is nondestructive. Automatic measuring equipment guarantees instantaneous measurement results that can be recorded in the field.     

Nimbus 7 SMMR Investigation of Snowpack Properties in the Northern Great Plains for the Winter of 1978-1979

An investigation of the capabilities of remote sensing of snowpack properties was conducted with brightness temperatures from the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and climatological data for the northern Great Plains for the winter of 1978-1979. The radiometer data included horizontally and vertically polarized brightness temperatures at the 0.81-, 1.66-, and 2.80-, and 4.54-cm wavelengths for both day and night overpasses, with a repeat coverage on the average of every two to three days. The brightness temperatures in each channel and the daily surface climatological elements of maximum and minimum air temperature, precipitation, snowfall, and snow depth were objectively analyzed to a 20-km grid with 35 rows and 42 columns. The analysis concentrated on temporal analyses of selected grid cells. Characteristic signatures were observed for initial snow accumulation, snow depth to about 20 cm, beginning of snow melting in the surface layers, and snow melt. The process of snow ripening was evident in the thawing and refreezing cycles of the snow surface layers. Discrimination of dry soil, wet soil, snow amount to 15 cm, and liquid water at the soil surface before runoff occurred was present with the use of both polarizations at the 0.81- and 1.66-cm wavelengths, although the longer wavelengths contained additional information on the state of the surface underlying the snow pack.     

A semantic-based architecture for sensor networks

With rapid development of sensor networks technology, it becomes feasible to deploy multiple sensor networks in relevant area to collect interested information. Sensor nodes that are co-located but belong to different sensor networks may not be able to collaborate properly to gain the capacity or performance. In this paper we propose a semantic-based sensor networks architecture that enables inter-networking of sensor networks. In this Semantic Sensor Net (Ssn), a semantic tag is attached to the sensory data so that the sensor networks are able to exchange information and work collaboratively. The process of semantic creation and maintenance is described. We also introduce the concept of InterSensorNet. This infrastructure enables efficient information exchange and information extraction among multiple sensor networks.     

Current-Mirror-Based Potentiostats for Three-Electrode Amperometric Electrochemical Sensors

We present a new circuit topology for potentiostats that interface with three-electrode amperometric electrochemical sensors. In this new topology, a current-copying circuit, e.g., a current mirror, is placed in the sensor current path to generate a mirrored image of the sensor current. The mirrored image is then measured and processed instead of the sensor current itself. The new potentiostat topology consumes very low power, occupies a very small die area, and has potentially very low noise. These characteristics make the new topology very suitable for portable or bioimplantable applications. In order to demonstrate the feasibility of the new topology, we present the results of a potentiostat circuit implemented in a 0.18- $mu{hbox {m}}$ CMOS process. The circuit converts the sensor current to a frequency-modulated pulse waveform, for which the time difference between two consecutive pulses is inversely proportional to the sensor current. The potentiostat measures the sensor current from 1 nA to 1 $mu{hbox {A}}$ with better than 0.1% of accuracy. It consumes only 70 $muhbox{W}$ of power from a 1.8-V supply voltage and occupies an area of 0.02 ${hbox {mm}}^{2}$.      

一种基于单片机智能控制的云台设计

为实现监控更智能和节能的目的,采用单片机与传感器结合控制步进电机转动的方法,设计一种以单片机为核心的智能控制云台。该云台只在有人进入传感器监测区域时才工作,且能根据传感器采集的信息计算出人的位置,从而控制云台转动,实现自动追踪监控,无需人控制,成本低,与现在市面上的云台相比更智能和节能,有很强的实用价值。在研究分析后做出了实物,经测试该云台能实现有人时工作和自动追踪监控,验证了方案的可行性。     

Measurement of directional and spectral signatures of light reflectance by snow

The bidirectional reflection distribution functions (BRDF) of snow have been measured at high spectral resolution at various locations in Finland (Vuotso, Hyytia/spl uml/la/spl uml/, Sodankyla/spl uml/, Kilpisja/spl uml/rvi, Rovaniemi, Sodankyla/spl uml/ again). The measured snow types include fresh, new snow, both needle-like and hexagonal flakes, old, loose snow, and melting and refrozen snow. All snow types show strong forward scattering as previously reported, but there also appeared to be some enhancement in the backward directions that has not been reported in much detail. The grain size gives a clear signal at near-infrared, which was observed previously. A nontrivial dependence on grain shape was also observed, which has been ignored previously. Melting snow has a distinct forward feature not observable in dry snow: first a maximum in specular direction, a minimum after that, and then again brightening forward. There is a spectral signal at 1250/1350 nm that could be useful for wetness recovery in particular, even when the topography or BRDF model is not known. Density dependence was observed, partially contradicting earlier measurements. Microtopographic roughness slightly increases backscattering as expected. Much more detailed information about snow could be observed using hyperspectral, multidirectional remote sensing techniques than with current instruments. Measurements of more snow types need to be taken, especially dirty snow, snow/vegetation composites, and rough snow surfaces.     

The GLOBCARBON Cloud Detection System for the Along-Track Scanning Radiometer (ATSR) Sensor Series

The GLOBCARBON initiative aims to develop a service to generate fully calibrated estimates of at-land products using, in combination, data from the Along-Track Scanning Radiometer (ATSR) sensor series, MERIS and VEGETATION. A fundamental requirement of such processing is the effective removal of extraneous effects introduced by cloud, cloud shadow, snow, and atmosphere in these data. This paper reports on the implementation and extension of a new cloud and snow processing system for land, which is for the ATSR sensor series. The snow processor is as implemented for the Moderate Resolution Imaging Spectrometer, whereas the cloud processor is based on APOLLO, with extensions to generate the probabilities of cloud presence and a further test to eliminate the cloud missed by APOLLO. The system was tested on 48 images representing a wide range of cloud types and forms over three contrasting biomes-boreal forest, desert savannah, and tropical evergreen forest. Excellent results are produced and represent a major improvement over the original flagging system. The approach has been adopted in GLOBCARBON for the processing of 500 000 ATSR scenes and 20 000 AATSR striplines.     

The potential of times series of C-Band SAR data to monitor dry andshallow snow cover

A study was conducted to assess the potential of C-band synthetic aperture radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE<20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0°C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen     

Metamorphic signature of snow revealed in SSM/I measurements

Brightness temperatures (19, 22, 37, 85 GHz) measured by the special sensor microwave/imager (SSM/I) are analyzed using data from the snow monitoring network within the former Soviet Union during the 1987-1988 winter period. It is shown that in the beginning of winter, the SSM/I measurements display the classical snow scattering signature, i.e., the brightness temperatures decrease with increasing depth, and the largest decrease occurs at the highest frequency. Dramatic deviations from this pattern are observed in the middle of winter, where the brightness temperature approaches a minimum and then begins to increase despite the fact that the snow depth remains constant or continues to grow. The two-stream radiative transfer model is combined with results from dense media theory to help explain the phenomenon. Model results suggest that the increase in brightness temperature is due to a decrease of the single scattering albedo as the snowpack ages. This decrease of the albedo is related to changes in the snow crystalline structure due to metamorphism. Consequences for the interpretation of satellite measurements and development of algorithms for deriving snow water equivalent are discussed