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1.
A network of 30 standalone snow monitoring stations was used to investigate the snow cover distribution, snowmelt dynamics, and runoff generation during two rain‐on‐snow (ROS) events in a 40 km2 montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent (SWE) distribution within the catchment was applied on an hourly basis for two significant ROS flood events that occurred in December 2012. The available snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for the runoff generation. The study provides a spatially and temporally distributed picture of how the two observed ROS floods developed in the catchment. It became evident that the retention capacity of the snow cover is a crucial mechanism during ROS. It took several hours before water was released from the snowpack during the first ROS event, while retention storage was exceeded within 1 h from the start of the second event. Elevation was the most important terrain feature. South‐facing terrain contributed more water for runoff than north‐facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of snowmelt together with liquid precipitation for the generation of flood runoff during ROS and the large temporal and spatial variability of the relevant processes. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

2.
S. Pohl  P. Marsh 《水文研究》2006,20(8):1773-1792
Arctic spring landscapes are usually characterized by a mosaic of coexisting snow‐covered and bare ground patches. This phenomenon has major implications for hydrological processes, including meltwater production and runoff. Furthermore, as indicated by aircraft observations, it affects land‐surface–atmosphere exchanges, leading to a high degree of variability in surface energy terms during melt. The heterogeneity and related differences when certain parts of the landscape become snow free also affects the length of the growing season and the carbon cycle. Small‐scale variability in arctic snowmelt is addressed here by combining a spatially distributed end‐of‐winter snow cover with simulations of variable snowmelt energy balance factors for the small arctic catchment of Trail Valley Creek (63 km2). Throughout the winter, snow in arctic tundra basins is redistributed by frequent blowing snow events. Areas of above‐ or below‐average end‐of‐winter snow water equivalents were determined from land‐cover classifications, topography, land‐cover‐based snow surveys, and distributed surface wind‐field simulations. Topographic influences on major snowmelt energy balance factors (solar radiation and turbulent fluxes of sensible and latent heat) were modelled on a small‐scale (40 m) basis. A spatially variable complete snowmelt energy balance was subsequently computed and applied to the distributed snow cover, allowing the simulation of the progress of melt throughout the basin. The emerging patterns compared very well visually to snow cover observations from satellite images and aerial photographs. Results show the relative importance of variable end‐of‐winter snow cover, spatially distributed melt energy fluxes, and local advection processes for the development of a patchy snow cover. This illustrates that the consideration of these processes is crucial for an accurate determination of snow‐covered areas, as well as the location, timing, and amount of meltwater release from arctic catchments, and should, therefore, be included in hydrological models. Furthermore, the study shows the need for a subgrid parameterization of these factors in the land surface schemes of larger scale climate models. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

3.
Based on snow- and ice-thickness measurements at >11 000 points augmented by snow- and icecore studies during 4 expeditions from 1986 - 92 in the Weddell Sea, we describe characteristics and distribution patterns of snow and meteoric ice and assess their importance for the mass balance of sea ice. For first-year ice (FY) in the central and eastern Weddell Sea, mean snow depth amounts to 0.16 m (mean ice thickness 0.75 m) compared to 0.53 m (mean ice thickness 1.70 m) for second-year ice (SY) in the northwestern Weddell Sea. Ridged ice retains a thicker snow cover than level ice, with ice thickness and snow depth negatively correlated for the latter, most likely due to aeolian redistribution. During the different expeditions, 8, 15, 17 and 40% of all drill holes exhibited negative freeboard. As a result of flooding and brine seepage into the snow pack, snow salinities averaged 4‰. Through 18O measurements the distribution of meteoric ice (i.e. precipitation) in the sea-ice cover was assessed. Roughly 4% of the total ice thickness consist of meteoric ice (FY 3%, SY 5%). With a mean density of 290 kg/m3, the snow cover itself contributes 8% to total ice mass (7% FY, 11% SY). Analysis of 18O in snow indicates a local maximum in accumulation in the 65 to 75^S latitude zone. Hydrogen peroxide in the snow has proven useful as a temporal tracer and for identification of second-year floes. Drawing on accumulation data from stations at the Weddell Sea coast, it becomes clear that the onset of ice growth is important for the evolution of ice thickness and the interaction between ice and snow. Loss of snow to leads due to wind drift may be considerable, yet is reduced owing to metamorphic processes in the snow column. This is confirmed by a comparison of accumulation data from coastal stations and from snow depths over sea ice. Temporal and spatial accumulation patterns of snow are shown to be important in controlling the sea-ice cover evolution.  相似文献   

4.
Snow 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 & Sons, Ltd.  相似文献   

5.
The Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large-scale processes that influence the timing of these snow-induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub-basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate-resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer-term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km2/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large-scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long-term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.  相似文献   

6.
Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm3 was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.  相似文献   

7.
Abstract

This paper presents the relationship between Indian summer monsoon total rainfall and two parameters from Eurasian snow cover, one being the winter snow cover extent and the other the area of spring snowmelt. Satellite-derived Eurasian snow cover extent and Indian monsoon rainfall data were obtained from the NOAA/NESDIS and the India Meteorological Department (IMD) for the period 1966–1985. Seasonal cyclic variations of snow cover showed a higher swing in both the winter and the spring seasons of the cycle as compared to the remaining seasons of the year in the lower region of the cycle. The established inverse relation between winter snow cover and monsoon rainfall during June to September is further extended. Winter snow cover is very strongly correlated with spring snowmelt over Eurasia. Spring snowmelt area is obtained by subtracting the May snow cover extent from that of the previous February. The variations of spring snowmelt were also compared with Indian total monsoon rainfall. The detected correlation is stronger between snowmelt and monsoon rainfall than between the winter snow cover and the monsoon rainfall. There is also a significant multiple correlation among winter snow cover, spring snowmelt and monsoon rainfall. Lastly, a significant multiple correlation suggested a multiple regression equation which might improve the climatic prediction of monsoon rainfall over India.  相似文献   

8.
Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

9.
Seasonal snow cover in mountainous regions will affect local climate and hydrology. In this study, we assessed the role of altitude in determining the relative importance of temperature and precipitation in snow cover variability in the Central Tianshan Mountains. The results show that: (a) in the study area, temperature has a greater influence on snow cover than precipitation during most of the time period studied and in most altitudes. (b) In the high elevation area, there is a threshold altitude of 3,900 ± 400 m, below which temperature is negatively correlated whereas precipitation is positively correlated to snow cover, and above which the situation is the opposite. Besides, this threshold altitude decreases from snow accumulated period to snow stable period and then increases from snowmelt period to snow‐free period. (c) Below 2,000 m, there is another threshold altitude of 1,400 ± 100 m during the snow stable period, below (above) which precipitation (temperature) is the main driver of snow cover.  相似文献   

10.
A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R2 above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.  相似文献   

11.
We analyse spatial variability and different evolution patterns of snowpack in a mixed beech–fir stand in the central Pyrenees. Snow depth and density were surveyed weekly along six transects of contrasting forest cover during a complete accumulation and melting season; we also surveyed a sector unaffected by canopy cover. Forest density was measured using the sky view factor (SVF) obtained from digital hemispherical photographs. During periods of snow accumulation and melting, noticeable differences in snow depth and density were found between the open site and those areas covered by forest canopy. Principal component analysis provided valuable information in explaining these observations. The results indicate a high variability in snow accumulation within forest areas related to differences in canopy density. Maximum snow water equivalent (SWE) was reduced by more than 50% beneath dense canopies compared with clearings, and this difference increased during the melting period. We also found significant temporal variations: when melting began in sectors with low SVF, most of the snow had already thawed in areas with high SVF. However, specific conditions occasionally produced a different response of SWE to forest cover, with lower melting rates observed beneath dense canopies. The high values of correlation coefficients for SWE and SVF (r > 0·9) indicate the reliability of predicting the spatial distribution of SWE in forests when only a moderate number of observations are available. Digital hemispherical photographs provide an appropriate tool for this type of analysis, especially for zenith angles in the range 35–55 . Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

12.
Application of snowmelt runoff model for water resource management   总被引：1，自引：0，他引：1  
Snow‐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 & Sons, Ltd.  相似文献   

13.
Abstract

The physical properties of snow, including apparent density, snow cover distribution and snowmelt in the Nahr El Kelb basin (Mount Lebanon), were studied in order to design a simple empirical snowmelt model. In February 2001, snow covered an area of 1600 km2 on Mount Lebanon, representing a water equivalent of 1.1 x 109 m3. The snow surface area was calculated by combining TM5 images with a digital elevation model, and field observations made every three days, from 1400 to 2300 m altitude. The depletion of snow cover was measured from the end of December 2000 to the end of June 2001. The snowmelt was measured from surface depletion on a degree-day basis. A simple model relating the daily snowmelt to the product of wind speed and average positive daily air temperature, is presented and discussed. For Mount Lebanon, this model gave a better approximation of snowmelt than a simple degree-day model.  相似文献   

14.
Snow in the McMurdo Dry Valleys is a potential source of moisture for subnivian soils in a cold desert ecosystem. In a water‐limited environment, enhanced soil moisture is expected to provide more favourable conditions for subnivian soil communities. In addition, snow cover insulates the underlying soil from air temperature extremes. Quantifying the spatial and temporal patterns of seasonal snow accumulation and ablation is necessary to understand these dynamics. Repeat high‐resolution imagery acquired for the 2009–2010 austral summer was used to map the seasonal distribution of snow across Taylor and Wright valleys, Southern Victorialand, Antarctica. An edge detection algorithm was used to perform an object‐based classification of snow‐covered area. Coupled with topographic parameters obtained from a 30‐m digital elevation model, unique distribution patterns were characterized for five regions within the neighbouring valleys. Time lapses of snow distribution in each region provide insight into spatially variable aerial ablation rates (change in area of landscape covered by snow) across the region. A strong coastal to interior gradient of decreasing snow‐covered area was evident for both Taylor and Wright valleys. The surrounding regions of Lake Fryxell, Lake Hoare, Lake Bonney, Lake Brownworth, and Lake Vanda exhibited losses of snow‐covered area of 9.61 km2 (?93%), 1.63 km2 (?72%), 1.07 km2 (?97%), 2.60 km2 (?82%), and 0.25 km2 (?96%), respectively, as measured from peak accumulation in October to mid‐January. Differences in aerial ablation rates within and across local regions suggest that both topographic variation and regional microclimates influence the ablation of seasonal snow cover. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

15.
Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb18h–Tb36h) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

16.
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.  相似文献   

17.
Seasonal snowpack dynamics are described through field measurements under contrasting canopy conditions for a mountainous catchment in the Japan Sea region. Microclimatic data, snow accumulation, albedo and lysimeter runoff are given through the complete winter season 2002–03 in (1) a mature cedar stand, (2) a larch stand, and (3) a regenerating cedar stand or opening. The accumulation and melt of seasonal snowpack strongly influences streamflow runoff during December to May, including winter baseflow, mid‐winter melt, rain on snow, and diurnal peaks driven by radiation melt in spring. Lysimeter runoff at all sites is characterized by constant ground melt of 0·8–1·0 mm day−1. Rapid response to mid‐winter melt or rainfall shows that the snowpack remains in a ripe or near‐ripe condition throughout the snow‐cover season. Hourly and daily lysimeter discharge was greatest during rain on snow (e.g. 7 mm h−1 and 53 mm day−1 on 17 December) with the majority of runoff due to rainfall passing through the snowpack as opposed to snowmelt. For both rain‐on‐snow and radiation melt events lysimeter discharge was generally greatest at the open site, although there were exceptions such as during interception melt events. During radiation melt instantaneous discharge was up to 4·0 times greater in the opening compared with the mature cedar, and 48 h discharge was up to 2·5 times greater. Perhaps characteristic of maritime climates, forest interception melt is shown to be important in addition to sublimation in reducing snow accumulation beneath dense canopies. While sublimation represents a loss from the catchment water balance, interception melt percolates through the snowpack and contributes to soil moisture during the winter season. Strong differences in microclimate and snowpack albedo persisted between cedar, larch and open sites, and it is suggested further work is needed to account for this in hydrological simulation models. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

18.
Snow is an important component of the Earth's climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This study's results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.  相似文献   

19.
The spatial and temporal characterization of geochemical tracers over Alpine glacierized catchments is particularly difficult, but fundamental to quantify groundwater, glacier melt, and rain water contribution to stream runoff. In this study, we analysed the spatial and temporal variability of δ2H and electrical conductivity (EC) in various water sources during three ablation seasons in an 8.4‐km2 glacierized catchment in the Italian Alps, in relation to snow cover and hydro‐meteorological conditions. Variations in the daily streamflow range due to melt‐induced runoff events were controlled by maximum daily air temperature and snow covered area in the catchment. Maximum daily streamflow decreased with increasing snow cover, and a threshold relation was found between maximum daily temperature and daily streamflow range. During melt‐induced runoff events, stream water EC decreased due to the contribution of glacier melt water to stream runoff. In this catchment, EC could be used to distinguish the contribution of subglacial flow (identified as an end member, enriched in EC) from glacier melt water to stream runoff, whereas spring water in the study area could not be considered as an end member. The isotopic composition of snow, glacier ice, and melt water was not significantly correlated with the sampling point elevation, and the spatial variability was more likely affected by postdepositional processes. The high spatial and temporal variability in the tracer signature of the end members (subglacial flow, rain water, glacier melt water, and residual winter snow), together with small daily variability in stream water δ2H dynamics, are problematic for the quantification of the contribution of the identified end members to stream runoff, and call for further research, possibly integrated with other natural or artificial tracers.  相似文献   

20.
The temporal and spatial continuity of spatially distributed estimates of snow‐covered area (SCA) are limited by the availability of cloud‐free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt–Verde watershed of Arizona. Gridded positive accumulated degree‐days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

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