A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

Remote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.     

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite-based SWE data. Stronger relationships were found in snow-dominated basins (R_s > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non-SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water-Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.     

On the evaluation of snow water equivalent estimates over the terrestrial Arctic drainage basin

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

Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution,multi-sensor comparison

Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). In this reconstruction approach, modeled snowmelt over each pixel is integrated during the period of satellite-observed snow cover to estimate SWE. Due to underestimates in snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS snow cover products.     

SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.     

Tree‐ring records unveil long‐term influence of the Pacific Decadal Oscillation on snowpack dynamics in the Stikine River basin,northern British Columbia

Long hydroclimate records are essential elements for the assessment and management of changing freshwater resources. These records are especially important in transboundary watersheds where international cooperation is required in the joint planning and management process of shared basins. Dendrochronological techniques were used to develop a multicentury record of April 1 snow water equivalent (SWE) for the Stikine River basin in northern British Columbia, Canada, from moisture‐sensitive white spruce (Picea glauca) tree rings. Explaining 43% of the instrumental SWE variability, to our knowledge, this research represents the first attempt to develop long‐term snowpack reconstructions in northern British Columbia. The results indicated that 15 extreme low April 1 SWE events occurred from 1789 to the beginning of the instrumental record in 1974. The reconstruction record also shows that the occurrence of hydrological extremes in the Stikine River basin is characterized by persistent below‐average periods in SWE consistent with phase shifts of the Pacific Decadal Oscillation (PDO). Spectral analyses indicate a very distinct in‐phase (positive) relationship between the multidecadal frequencies of variability (~40 years) extracted from the SWE tree‐ring reconstruction and other reconstructed winter and spring PDO indices. Comparison of the reconstructed SWE record with other tree‐ring‐derived PDO proxy records shows coherence at multidecadal frequencies of variability. The research has significant implications for regional watershed management by highlighting the hydrological response of the Stikine River basin to prior climate changes.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

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

New snow metrics for a warming world

Snow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station-based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi-use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi-temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At-Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow-related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground-based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground-based network of data, so these MODIS-derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid- and late-century.     

Investigation of the snow‐cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow‐cover products and hydrometeorological data

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.     

卫星遥感藏北积雪分布及影响因子分析

利用1993~2004年SSM/I被动微波辐射仪反演的雪深资料,1996~2004年NOAA/AVHRR可见光和红外反演的积雪覆盖面积资料,1966~2003年藏北地区6个地面台站的积雪观测资料来检验卫星资料的可用性,并研究近年来藏北积雪的时空分布和影响因素.结果表明,SSM/I, NOAA/AVHRR和实际观测的积雪资料具一致性.从积雪时间变化看：季节尺度上,藏北地区秋冬季积雪迅速增加,但春季(3~5月)融雪速度不快,呈现正反馈特征;年际尺度上,藏北地区20世纪60年代末期起积雪开始减少,80年代积雪增加,90年代起到2003年积雪总体上减少,呈现出减少—增加—减少趋势.采用小波分析发现积雪振荡周期存在着一个准2~3年,准9年和13年的周期,从20世纪70年代初到90年代中期还有一个5年的周期.积雪空间上看,藏北地区积雪主要集中在东部地区,该区每个冬春年积雪覆盖旬数超过15旬,显著高于西部少雪区,大部分积雪集中在4900~5600 m的高度左右;藏北高原积雪变动的显著区位于藏北中东部的安多和聂荣地区.利用藏北地区1966~2003年的地面温度和降水资料建立回归方程模拟年累积雪日,结果表明模拟值与实测值之间的相关系数达0.74.积雪时空分布受温度、降水等因子影响明显.1998~2003年藏北积雪的减少与全球变暖有关,但降水的减少可能是导致近年来藏北积雪减少的更主要因素.     

Ice and Snow Cover of Continental Water Bodies from Simultaneous Radar Altimetry and Radiometry Observations

We show how the studies of ice and snow cover of continental water bodies can benefit from the synergy of more than 15 years-long simultaneous active (radar altimeter) and passive (radiometer) observations from radar altimetric satellites (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On) and how this approach can be complemented by SSM/I passive microwave data to improve spatial and temporal coverage. Five largest Eurasian continental water bodies—Caspian and Aral seas, Baikal, Ladoga and Onega lakes are selected as examples. First we provide an overview of ice regime and history of ice studies for these seas and lakes. Then a summary of the existing state of the art of ice discrimination methodology from altimetric observations and SSM/I is given. The drawbacks and benefits of each type of sensor and particularities of radiometric properties for each of the chosen water bodies are discussed. Influence of sensor footprint size, ice roughness and snow cover on satellite measurements is also addressed. A step-by-step ice discrimination approach based on a combined use of the data from the four altimetric missions and SSM/I is presented, as well as validation of this approach using in situ and independent satellite data in the visible range. The potential for measurement of snow depth on ice from passive microwave observations using both altimeters and SSM/I is addressed and a qualitative comparison of in situ snow depth observations and satellite-derived estimates is made.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins

ABSTRACT

In snow-dominated basins, collection of snow data while capturing its spatio-temporal variability is difficult; therefore, integrating assimilation products could be a viable alternative for improving streamflow simulation. This study evaluates the accuracy of daily snow water equivalent (SWE) provided by the SNOw Data Assimilation System (SNODAS) of the National Weather Service at a 1-km² resolution for two basins in eastern Canada, where SWE is a critical variable intensifying spring runoff. A geostatistical interpolation method was used to distribute snow observations. SNODAS SWE products were bias-corrected by matching their cumulative distribution function to that of the interpolated snow. The corrected SWE was then used in hydrological modelling for streamflow simulation. The results indicate that the bias-correction method significantly improved the accuracy of the SNODAS products. Moreover, the corrected SWE improved the simulation performance of the peak values. Although the uncertainty of SNODAS estimates is high for eastern Canadian basins, they are still of great value for regions with few snow stations.     

Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin,Arizona

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.     

Assessment of glacier- and snowmelt-driven streamflow in the arid middle Tianshan Mountains of China

Due to limitations in transport and communication infrastructures and difficulties in accessing glaciers, it is challenging to monitor snow and glaciers. In this study, the enhanced Utah Energy Balance (UEB) with a glacier melt model and snow above and below the forest ablation algorithm is used to assess the contributions of snow and glacier melting in three typical inland river basins (MRB, URB and KRB) in the middle Tianshan Mountains of China from 2002 to 2014. Forced by the spatial downscaling of the China meteorological forcing dataset (CMFD) coupled with other parameters, the model simulates the total surface water balance using surface water input from snowmelt, glacial melt and rainfall. Model simulations reveal that although the MRB, URB and KRB are all located on the northern slopes of the Tianshan Mountains, there are obvious differences in their water resource composition characteristics. Different from the URB, which is mainly replenished by glacial melt and had an average annual percentage of glacial melt of approximately 39% of the total surface water from 2009 to 2014, the MRB and KRB are mainly supplied by snowmelt and rainfall and both had an average annual percentage of snowmelt of approximately 37%. Although snowmelt is an important source of water to inland rivers, especially during the snowmelt season, the contributions of snowmelt in these three basins are very small especially for the URB, which had a contribution of 17%. This study effectively verifies the applicability of the CMFD and provides important scientific and technological support for determining the spatiotemporal variations in snow and glacial melt in the middle Tianshan Mountains, where meteorological observation data are scarce and some observational data, such as radiation data, are incomplete.     

Snow density variations: consequences for ground‐penetrating radar

Reliable hydrological forecasts of snowmelt runoff are of major importance for many areas. Ground‐penetrating radar (GPR) measurements are used to assess snowpack water equivalent for planning of hydropower production in northern Sweden. The travel time of the radar pulse through the snow cover is recorded and converted to snow water equivalent (SWE) using a constant snowpack mean density from the drainage basin studied. In this paper we improve the method to estimate SWE by introducing a depth‐dependent snowpack density. We used 6 years measurements of peak snow depth and snowpack mean density at 11 locations in the Swedish mountains. The original method systematically overestimates the SWE at shallow depths (+25% for 0·5 m) and underestimates the SWE at large depths (?35% for 2·0 m). A large improvement was obtained by introducing a depth–density relation based on average conditions for several years, whereas refining this by using separate relations for individual years yielded a smaller improvement. The SWE estimates were substantially improved for thick snow covers, reducing the average error from 162 ± 23 mm to 53 ± 10 mm for depth range 1·2–2·0 m. Consequently, the introduction of a depth‐dependent snow density yields substantial improvements of the accuracy in SWE values calculated from GPR data. Copyright © 2005 John Wiley & Sons, Ltd.     

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

Effects of land cover change on streamflow in the interior Columbia River Basin (USA and Canada)

An analysis of the hydrological effects of vegetation changes in the Columbia River basin over the last century was performed using two land cover scenarios. The first was a reconstruction of historical land cover vegetation, c. 1900, as estimated by the federal Interior Columbia Basin Ecosystem Management Project (ICBEMP). The second was current land cover as estimated from remote sensing data for 1990. Simulations were performed using the variable infiltration capacity (VIC) hydrological model, applied at one‐quarter degree spatial resolution (approximately 500 km² grid cell area) using hydrometeorological data for a 10 year period starting in 1979, and the 1900 and current vegetation scenarios. The model represents surface hydrological fluxes and state variables, including snow accumulation and ablation, evapotranspiration, soil moisture and runoff production. Simulated daily hydrographs of naturalized streamflow (reservoir effects removed) were aggregated to monthly totals and compared for nine selected sub‐basins. The results show that, hydrologically, the most important vegetation‐related change has been a general tendency towards decreased vegetation maturity in the forested areas of the basin. This general trend represents a balance between the effects of logging and fire suppression. In those areas where forest maturity has been reduced as a result of logging, wintertime maximum snow accumulations, and hence snow available for runoff during the spring melt season, have tended to increase, and evapotranspiration has decreased. The reverse has occurred in areas where fire suppression has tended to increase vegetation maturity, although the logging effect appears to dominate for most of the sub‐basins evaluated. Predicted streamflow changes were largest in the Mica and Corralin sub‐basins in the northern and eastern headwaters region; in the Priest Rapids sub‐basin, which drains the east slopes of the Cascade Mountains; and in the Ice Harbor sub‐basin, which receives flows primarily from the Salmon and Clearwater Rivers of Idaho and western Montana. For these sub‐basins, annual average increases in runoff ranged from 4·2 to 10·7% and decreases in evapotranspiration ranged from 3·1 to 12·1%. In comparison with previous studies of individual, smaller sized watersheds, the modelling approach used in this study provides predictions of hydrological fluxes that are spatially continuous throughout the interior Columbia River basin. It thus provides a broad‐scale framework for assessing the vulnerability of watersheds to altered streamflow regimes attributable to changes in land cover that occur over large geographical areas and long time‐frames. Copyright © 2000 John Wiley & Sons, Ltd.     

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.