Snow Cover Depletion Curves Research Articles

Impact of climate change on snow covered area over the transboundary Jhelum River basin of Himalayas region using Remote Sensing

Climate change is one of the pertinent issues for the 21st century mainly for the transboundary river basins because it has parallel impacts on the hydrosphere and cryosphere. The temperature changes impacted the snow-covered area (SCA) of the river basins. MODIS MOD10A2 product data was used to delineate the SCA for the Jhelum River basin. The catchment is further divided into elevation zones using DEM to delineate the SCA for every zone. The temperature changes were plotted against the SCA, and correlation analysis was used to see the impact of climate change on the SCA. The seasonal trend of SCA has depicted an increase in SCA during the spring and summer seasons and decreasing trend during autumn while winter shows stability. The snow cover depletion curve (SCDC) showed that snow is maximum in December, January, and February while its minimum during July and August. The correlation analysis of temperature and SCA for the period of 2001-2017 illustrated the inverse relationship, which depicted that a slight increase in temperature may trigger the fastest depletion in the snow-covered areas generating water issues for snow/glacier-fed river basins.

Analysis of topographic controls on depletion curves derived from airborne lidar snow depth data

Abstract The annual consistency of spatial patterns of snow accumulation and melt suggests that the evolution of these patterns, known as depletion curves, is useful for estimating basin water content and runoff prediction. Theoretical snow cover depletion curves are used in models to parameterize fractional snow-covered area (fSCA) based on modeled estimates of snow accumulation and snowmelt. Directly measuring the spatio-temporal snow distribution, characterization of depletion curves, and understanding how they vary across mountainous landscapes was not possible until the recent U.S. National Aeronautics and Space Administration (NASA) Airborne Snow Observatory (ASO). Herein, for the first time, high-resolution spatio-temporal snow depth information from the ASO is used to derive observation-based snow cover depletion curves across physiographic gradients by estimating the slope of the fSCA–snow depth relationship (i.e. depletion slope). The depletion slope reveals important insights into snow processes as it is strongly related to snow depth variability (r2 = 0.58). Regression tree analysis between observed depletion slopes and physiography, particularly vegetation height and terrain roughness, displays clear nonlinear dynamics and explains 31% of the variance in depletion slope. This unique observation-based analysis of snow cover depletion curves has implications for energy and water flux calculations across many earth system models.

Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies

Abstract. The spatial distribution of snow water equivalent (SWE) and melt are important for estimating areal melt rates and snow-cover depletion (SCD) dynamics but are rarely measured in detail during the late ablation period. This study contributes results from high-resolution observations made using large numbers of sequential aerial photographs taken from an unmanned aerial vehicle on an alpine ridge in the Fortress Mountain Snow Laboratory in the Canadian Rocky Mountains from May to July in 2015. Using structure-from-motion and thresholding techniques, spatial maps of snow depth, snow cover and differences in snow depth (dHS) during ablation were generated in very high resolution as proxies for spatial SWE, spatial ablation rates and SCD. The results indicate that the initial distribution of snow depth was highly variable due to overwinter snow redistribution; thus, the subsequent distribution of dHS was also variable due to albedo, slope/aspect and other unaccountable differences. However, the initial distribution of snow depth was 5 times more variable than that of the subsequent dHS values, which varied by a factor of 2 between the north and south aspects. dHS patterns were somewhat spatially persistent over time but had an insubstantial impact on SCD curves, which were overwhelmingly governed by the initial distribution of snow depth. The reason for this is that only a weak spatial correlation developed between the initial snow depth and dHS. Previous research has shown that spatial correlations between SWE and ablation rates can strongly influence SCD curves. Reasons for the lack of a correlation in this study area were analysed and a generalisation to other regions was discussed. The following questions were posed: what is needed for a large spatial correlation between initial snow depth and dHS? When should snow depth and dHS be taken into account to correctly model SCD? The findings of this study suggest that hydrological and atmospheric models need to incorporate realistic distributions of SWE, melt energy and cold content; therefore, they must account for realistic correlations (i.e. not too large or too small) between SWE and melt in order to accurately model SCD.

Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator

Abstract During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.

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.

Detection of earlier snowmelt in the Wind River Range, Wyoming, using Landsat imagery, 1972–2013

In the western United States snow has been melting earlier in recent decades due to warmer winter and spring weather. This is particularly noticeable in the Pacific Northwest and coastal areas, yet has been less obvious in locations farther inland. Using the historical Landsat image archive, snow cover was mapped in the Wind River Range (WRR) in northwestern Wyoming, from 1972–2013. The objective of this work was to estimate the temporal change in the rate of snowmelt in the Fremont Lake basin of the WRR for the 42-year study period. Much of the streamflow in Wyoming originates from melting snow in the WRR. Streamflow is a significant contributor to the water resources for the north-central part of the state and has tremendous societal and economic impacts especially during the prolonged drought that is affecting the western U.S. Consistent with the ongoing and severe drought, data from the Pine Creek Above Fremont Lake gauge show a striking reduction in cumulative stream discharge in the 2000s vs. the decades of the 1970s, 1980s and 1990s. Snow-cover depletion curves derived from snow maps created from Landsat imagery were generated for the period 1972–2013. MODerate-Resolution Imaging Spectroradiometer (MODIS)-derived standard snow-cover maps were also used to generate snow-cover depletion curves, from 2000–2013, to provide an accuracy assessment of the Landsat technique. Landsat-derived mean snow-cover depletion curves from 2000–2013 and from the three previous decades, show that snow cover in the Fremont Lake basin is melting 16±10days earlier, on average, in the 2000s compared to the period from 1972–1999. Increasing spring and summer nighttime air temperature is the likely driver of the earlier snowmelt documented in the Landsat record.

Assessment of future climate change impacts on snowmelt and stream water quality for a mountainous high-elevation watershed using SWAT

The Soil and Water Assessment Tool model was applied to assess the potential climate change impact on snowmelt and the non-point source pollution discharges in a 6,640.0 km2 high-elevation watershed of South Korea. For the snowmelt parameters of the model, Terra Moderate Resolution Imaging Spectroradiometer image was used to obtain the snow cover depletion curve. The model was calibrated using 11 years of data from 2000 to 2010 that included daily runoff and monthly sediment, total nitrogen (TN), and total phosphorus (TP). The climate change impacts on snowmelt in the watershed were evaluated for Special Report on Emission Scenarios A2, A1B, and B1, scenarios (HadCM3) and Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios (HadGEM3-RA). With the temperature increase of 4.33 °C during the snowmelt period in 2080s (2060–2099) RCP 8.5 scenario based on baseline period (1981–2010), the future snowmelt decreased to 39.9 % during snowmelt period (November–April). Turning the reduced snowmelt discharges into rain-runoff discharges under the 45.7 % increase of precipitation caused increase of future sediment, TN, and TP loads to 53.0, 118.2, and 137.5 % respectively. The future increases of TN and TP loads can stimulate the algal bloom and the eutrophication of the dam reservoir.

Introducing Hysteresis in Snow Depletion Curves to Improve the Water Budget of a Land Surface Model in an Alpine Catchment

Abstract The Durance watershed (14 000 km2), located in the French Alps, generates 10% of French hydropower and provides drinking water to 3 million people. The Catchment land surface model (CLSM), a distributed land surface model (LSM) with a multilayer, physically based snow model, has been applied in the upstream part of this watershed, where snowfall accounts for 50% of the precipitation. The CLSM subdivides the upper Durance watershed, where elevations range from 800 to 4000 m within 3580 km2, into elementary catchments with an average area of 500 km2. The authors first show the difference between the dynamics of the accumulation and ablation of the snow cover using Moderate Resolution Imaging Spectroradiometer (MODIS) images and snow-depth measurements. The extent of snow cover increases faster during accumulation than during ablation because melting occurs at preferential locations. This difference corresponds to the presence of a hysteresis in the snow-cover depletion curve of these catchments, and the CLSM was adapted by implementing such a hysteresis in the snow-cover depletion curve of the model. Different simulations were performed to assess the influence of the parameterizations on the water budget and the evolution of the extent of the snow cover. Using six gauging stations, the authors demonstrate that introducing a hysteresis in the snow-cover depletion curve improves melting dynamics. They conclude that their adaptation of the CLSM contributes to a better representation of snowpack dynamics in an LSM that enables mountainous catchments to be modeled for impact studies such as those of climate change.

Assessment of Snowmelt Impact on Chungju Dam Watershed Inflow Using Terra MODIS Data and SWAT Model

본 연구에서는 충주댐 유역(<TEX>$6,642.0km^2$</TEX>)을 대상으로 Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) 위성영상과 SWAT (Soil and Water Assessment Tool) 모형을 이용하여 융설이 댐유입량에 미치는 영향을 분석하였다. SWAT 모형의 융설 매개변수인 적설면적 감소곡선(Snow Cover Depletion Curve; SCDC)을 결정하기 위해 10 sets (2000-2010)의 Terra MODIS 위성영상으로부터 추출한 적설분포도와 지상 강설자료를 이용하여 구축한 적설심분포도(snow depth distribution; SDD)를 도출한 결과, 50% 적설면적일 때의 적설면적감소계수(sno50cov)는 0.4~0.7의 범위를 가지는 것으로 분석되었다. 매년의 융설 매개변수를 적용하여 융설기간 (11~4월)동안의 유출을 보정한 결과 평균 <TEX>$R^2$</TEX>는 0.54이었다. 10년 동안 융설기간의 유출량은 평균 104.3 mm로 연 유출량의 12.0%를 차지하는 것으로 분석되었다. This study is to evaluate the snowmelt impact on dam inflow for the Chungju Dam watershed <TEX>$6,642.0km^2$</TEX> using Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) and Soil and Water Assessment Tool (SWAT). To determine the SWAT snowmelt parameter; snow cover depletion curve (SCDC) the snow depth distribution (SDD) using Terra MODIS was used, the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 10 sets (2000-2010) data during snowmelt period (November-April), the sno50cov parameter, that is, the 50% coverage at a fraction of SCDC which determines the shape of snow depletion process, showed the values of 0.4 to 0.7. The SWAT model was calibrated with average <TEX>$R^2$</TEX> of 0.54 using the sno50cov of each year. The 10 years average streamflow during snowmelt period was 104.3 mm which covers 12.0% of the annual streamflow.

Implications of climate change on streamflow of a snow-fed river system of the Northwest Himalaya

Abstract Air temperature and snow cover variability are sensitive indicators of climate change. This study was undertaken to forecast and quantify the potential streamflow response to climate change in the Jhelum River basin. The implications of air temperature trends (+0.11°C/decade) reported for the entire north-west Himalaya for past century and the regional warming (+0.7°C/decade) trends of three observatories analyzed between last two decades were used for future projection of snow cover depletion and stream flow. The streamflow was simulated and validated for the year 2007–2008 using snowmelt runoff model (SRM) based on in-situ temperature and precipitation with remotely sensed snow cover area. The simulation was repeated using higher values of temperature and modified snow cover depletion curves according to the assumed future climate. Early snow cover depletion was observed in the basin in response to warmer climate. The results show that with the increase in air temperature, streamflow pattern of Jhelum will be severely affected. Significant redistribution of streamflow was observed in both the scenarios. Higher discharge was observed during spring-summer months due to early snowmelt contribution with water deficit during monsoon months. Discharge increased by 5%–40% during the months of March to May in 2030 and 2050. The magnitude of impact of air temperature is higher in the scenario-2 based on regional warming. The inferences pertaining to change in future streamflow pattern can facilitate long term decisions and planning concerning hydro-power potential, water resource management and flood hazard mapping in the region.

Application of snowmelt runoff model for water resource management

AbstractSnow‐covered areas (SCAs) are the fundamental source of water for the hydrological cycle for some region. Accurate measurements of river discharge from snowmelt can help manage much needed water required for hydropower generation and irrigation purposes. This study aims to apply the snowmelt runoff model (SRM) in the Upper Indus basin by the Astore River in northern Pakistan for the years 2000 to 2006. The Shuttle Radar Topographic Mission (SRTM) data are used to generate the Digital Elevation Model (DEM) of the region. Various variables (snow cover depletion curves (SCDCs), temperature and precipitation) and parameters (degree‐day factor, recession coefficient, runoff coefficients, time lag, critical temperature and temperature lapse rate) are used as input in the SRM. However, snow cover data are direct and an important input to the SRM. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the SCA. Normalized difference snow index (NDSI) algorithm is applied for snow cover mapping and to differentiate snow from other land features. Nash–Sutcliffe coefficient of determination (R2) and volume difference (DV) are used for quality assessment of the SRM. The results of the current research show that for the study years (2000–2006), the average value of R2 is 0·87 and average volume difference DV is 1·18%. The correlation coefficient between measured and computed runoff is 0·95. The results of the study further show that a high level of accuracy can be achieved during the snowmelt season. The simulation results endorse that the SRM in conjunction with MODIS snow cover product is very useful for water resource management in the Astore River and can be used for runoff forecasts in the Indus River basin in northern Pakistan. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Snow cover depletion under changed climate with the help of remote sensing and temperature data

Snow cover depletion curve (SDC) is one of the important variables in snow hydrological applications, and these curves are very much required for snowmelt runoff modeling in a snowfed catchment. Remote sensing is an important source of snow cover area which is used for preparation of SDC. Snow cover maps produced by Moderate Resolution Imaging Spectroradiometer (MODIS) satellites are one of the best source of satellite-based snow cover area at a regular interval. Therefore, in this study, snow cover maps have been prepared for the years 2000–2005 using MODIS data. The study area chosen viz. Beas basin up to Pandoh dam falls in western Himalayan region. For snowmelt runoff modeling, catchment is divided into number of elevation zones and SDC is required for each zone. When sufficient satellite data are not available due to cloud cover or due to some other reasons, then SDC can to be generated using temperature data. Under changed climate conditions also, modified SDC is required. Therefore, to have SDC under such situations, a relationship between snow cover area and cumulative mean temperature has been developed for each zone of the catchment. This procedure of having snow cover maps has two main purposes. First, it could potentially be used to generate snow cover maps when cloud-free satellite data are not available. Second, it can be used to generate snow-covered area in a new climate to see the impact of climate change on snowmelt runoff studies.

Evaluating the utility of the ANSA blended snow cover product in the mountains of eastern Turkey

Snow-covered area depletion curves represent a key input for snow run-off melting models, e.g. the snowmelt run-off model (SRM). SRM is a degree-day-based model for daily run-off simulations and forecasts in mountainous areas in which snowmelt is the major run-off contributor. Satellite images and aerial photographs are valuable sources for retrieving snow-covered area. The accuracy of snow cover mapping studies in the optical wavebands is highly dependent upon the algorithm's ability to detect clouds. On very cloudy days it is not possible to map snow cover using only optical sensors; however, microwave sensors can be used to obtain snow information on cloudy days. The snow-water equivalent (SWE) of a dry snowpack can be estimated with passive-microwave sensors such as Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E). Development of snow cover products based on multi-sensor data sources is needed for continuous regional and global snow cover mapping for climate, hydrological and weather applications. A preliminary blended snow product has been developed jointly by the US Air Force Weather Agency (AFWA) and NASA/Goddard Space Flight Center. The AFWA–NASA Snow Algorithm, or ANSA, blended snow product is an all-weather product that utilizes both visible and near-infrared (Moderate Resolution Imaging Spectroradiometer, MODIS) and microwave (Advanced Microwave Scanning Radiometer-Earth, AMSR-E) data. In this study the validation of the ANSA blended snow cover product, having 25 and 5 km resolution, respectively, was performed for the eastern part of Turkey for five months in the winter of 2007–2008. This is the first time that the ANSA snow cover product has been evaluated in a mountainous area, where the elevation ranges between 850 and 3000 m. Daily snow data collected at 36 meteorological stations were used in the analysis. Use of the ANSA snow products was found to improve the mapping of snow cover extent relative to using either MODIS or AMSR-E products alone, for the 2007–2008 winter in the eastern part of Turkey. 91% agreement was obtained between the ANSA snow maps and in situ observations for February. The lowest agreement percentage of 68% was obtained for March due to shallow snow depth and wetness of the snow. Change in the spatial resolution of the ANSA product from 25 km to 5 km increased the agreement percentages from 68% to 74% for March. ANSA prototype maps of 5 km resolution from February and March 2008 were used to derive snow cover depletion curves for the upper Euphrates basin located in the eastern part of Turkey. The results were compared with the curves obtained from MODIS daily snow products, and found to provide an improvement over using MODIS daily maps alone. This is because the ability of the microwave sensors to map snow through clouds provides snow cover information on cloudy days when the MODIS maps cannot, though at a coarser spatial resolution than can be obtained using MODIS.

Simulation of the snowmelt runoff contributing area in a small alpine basin

Abstract. Simulation of areal snowmelt and snowcover depletion over time can be carried out by applying point-scale melt rate computations to distributions of snow water equivalent (SWE). In alpine basins, this can be done by considering these processes separately on individual slope units. However, differences in melt timing and rates arise at smaller spatial scales due to the variability in SWE and snowpack cold content, which affects the timing of melt initiation, depletion of the snowcover and spatial extent of the snowmelt runoff contributing area (SRCA). This study examined the effects of variability in SWE, internal energy and applied melt energy on melt rates and timing, and snowcover depletion in a small cold regions alpine basin over various scales ranging from point to basin. Melt rate computations were performed using a physically based energy balance snowmelt routine (Snobal) in the Cold Regions Hydrological Model (CRHM) and compared with measurements at 3 meteorological stations over a ridge within the basin. At the point scale, a negative association between daily melt rates and SWE was observed in the early melt period, with deeper snow requiring greater energy inputs to initiate melt. SWE distributions over the basin (stratified by slope) were measured using snow surveys and repeat LiDAR depth estimates, and used together with computed melt rates to simulate the areal snowcover depletion. Comparison with observations from georeferenced oblique photographs showed an improvement in simulated areal snowcover depletion curves when accounting for the variability in melt rate with depth of SWE in the early melt period. Finally, the SRCA was characterized as the product of the snowcovered area and the fraction of the SWE distribution undergoing active melt and producing an appreciable runoff quantity on each slope unit. Results for each slope were then aggregated to give the basin scale SRCA. The SRCA is controlled by the variability of melt amongst slope units and over individual SWE distributions, the variability of SWE, and the resulting snowcover depletion patterns over the basin.

Modelling snow melt and snowcover depletion in a small alpine cirque, Canadian Rocky Mountains

AbstractSpatial and temporal patterns of areal snowcover depletion (SCD) were studied over a small (&lt;0·6 km2) alpine cirque within the Canadian Rocky Mountains using a combined approach of daily acquisition of remotely sensed imagery, together with meteorological observations and snowmelt modelling. Digital terrestrial photographs were georeferenced using a novel software tool together with a high‐resolution digital elevation model and used to derive measurements of fractional snowcovered area (SCA) over the cirque. Manual snow surveys carried out in the pre‐melt period were used to describe the initial frequency distribution of snow water equivalent (SWE) values over the cirque, and indicated a lognormal distribution of SWE when surveys were stratified by terrain features. Rates of snowmelt were simulated using a physically based snowmelt energy balance model, Snobal, driven by observed meteorological conditions at a nearby station, which were adjusted for slope orientation and exposure by making corrections to observed incoming shortwave and longwave radiation components in the cold regions hydrological model platform. Simulated melt rates were then applied to the approximated SWE distributions to model the decline in SCA over the spring. The model was found to perform well for the simulation of snowmelt based on point observations of SWE at the meteorological station, and produced a close correspondence between simulated and observed SCD curves representing two opposing slopes within the cirque. The results show that both the pre‐melt distributions of SWE and the spring melt rates exhibit considerable spatial variability between distinct slope units within the cirque, and that this variability has a significant impact on simulated SCD. Assuming a unimodal pre‐melt frequency distribution and conditions of spatially uniform snowmelt over complex terrain such as this can lead to large errors in the simulation results. It is suggested that modelling applications intended to represent snowmelt dynamics and areal SCD in similar alpine environments consider the effects of spatial variation in SWE distribution and melt energetics between slopes. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Spatial Variability of Shortwave Irradiance for Snowmelt in Forests

Abstract The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

AbstractSnow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km2) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Implications of spatial distributions of snow mass and melt rate for snow-cover depletion: theoretical considerations

AbstractSnow becomes patchy while melting, giving a heterogeneous surface with large contrasts in characteristics such as albedo. It is therefore necessary for surface schemes used in atmospheric and hydrological models to represent the influence of heterogeneities in snow cover on scales smaller than the model grid; this is typically done by introducing a snow-cover depletion (SCD) curve relating fractional snow cover to average depth. Here, ad hoc functional forms which have been used for this relationship are first compared with forms obtained for an assumed initial snow distribution undergoing homogeneous melt. The energy available for melt may be heterogeneous, however, and can be influenced by factors that also influence snow distribution, such as slope, aspect and vegetation cover. The modification of SCD curves by spatial variations in melt energy is investigated, and resulting curves are found to be similar to those which would be given by homogeneous melting of a snow cover with greater initial variance. This influence is increased by any negative correlation between snow depth and melt rate.

Tracing Technique of Atmospheric Waves with Wavelet Transformation

The few observed data related snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) building for 5 major watersheds in South Korea. Especially SDC is important parameter of snowmelt model.

Satellite-derived snow coverage related to hydropower production in Norway: Present and future

Hydropower derived from snow-melt runoff is a major source of electricity in Norway. Therefore, amount of snow-melt runoff is key to the prediction of available water. The prediction of water quantity may be accomplished through the use of hydrological models. These models, which may be run for individual basins, use satellite-derived snow-covered area in combination with snow-cover depletion curves. While it is known that snow albedo information would increase the accuracy of the models, large-scale albedo measurements have not yet been obtained from satellites on a regular basis. This paper presents Landsat-5 Thematic Mapper (TM) reflectances recorded in May 1989 from a mountainous catchment at Kvikne, Norway. Satellite-derived albedo values are analysed, and compared with simultaneously measured in situ albedo. The satellite-derived shortwave snow albedo is comparable with bare ground albedo and values as low as 0.19 were found in areas where the snow was highly metamorphosed and heavily blackened by organic material. To map snow-covered areas, the contrast between snow and snow-free areas can be improved by using a normalized TM Band 2-5 difference image. While TM Band 2 alone shows varying degrees of snow surface contamination within the study area, the normalized difference snow index (NDSI) is not affected by impurities. This paper also discusses the use of NASA's EOS (Earth Observing System) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is planned to be launched in the summer of 1999 for mapping of large-scale geophysical parameters including snow-cover. MODIS will enable snow cover and albedo to be mapped in Norway on a daily basis, and should enhance our ability to estimate snow coverage and thus manage hydropower production.