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1.
Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

2.
Snow accumulation and ablation rule the temporal dynamics of water availability in mountain areas and cold regions. In these environments, the evaluation of the snow water amount is a key issue. The spatial distribution of snow water equivalent (SWE) over a mountain basin at the end of the snow accumulation season is estimated using a minimal statistical model (SWE‐SEM). This uses systematic observations such as ground measurements collected at snow gauges and snow‐covered area (SCA) data retrieved by remote sensors, here MODIS. Firstly, SWE‐SEM calculates local SWE estimates at snow gauges, then the spatial distribution of SWE over a certain area using an interpolation method; linear regressions of the first two order moments of SWE with altitude. The interpolation has been made by both confining and unconfining the spatial domain by SCA. SWE‐SEM is applied to the Mallero basin (northern Italy) for calculating the snow water equivalent at the end of the winter season for 6 years (2001–2007). For 2007, SWE‐SEM estimates are validated through fieldwork measurements collected during an ‘ad hoc’ campaign on March 31, 2007. Snow‐surveyed measurements are used to check SCA, snow density and SWE estimates. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

3.
A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.  相似文献   

4.
In North American Land Data Assimilation System Phase 2 (NLDAS‐2) Noah simulation, the NLDAS team introduced an intermediate ‘fix’ to constrain the surface exchange coefficient when the atmospheric boundary layer is stable. In the current NLDAS‐2 Noah version, this fix is used for all stable cases including snow‐free grid cells. In this study, we simply apply this fix to the grid cells in which both stable atmospheric boundary layer and snow exist simultaneously, excluding the snow‐free grid cells as we recognize that the fix in NLDAS‐2 is too strong. We conduct a 31‐year (1979–2009) NLDAS‐2 Noah interim (Noah‐I) run and use observed streamflow, evapotranspiration, land surface temperature, soil temperature, and ground heat flux to evaluate the results, including comparisons with the original NLDAS‐2 Noah run. The results show that Noah‐I has the same performance as NLDAS‐2 Noah for snow water equivalent; however, Noah‐I significantly improved the simulation of other hydrometeorological products as noted earlier when compared with NLDAS‐2 Noah and the observations. This simple modification is being included in the next Noah version used in NLDAS. The hydrometeorological products from the improved NLDAS‐2 Noah‐I are being staged on the National Centers for Environmental Prediction public server. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

5.
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   

6.
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7.
Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

8.
Seasonal low flows are important for sustaining ecosystems and for supplying human needs during the dry season. In California's Sierra Nevada mountains, low flows are primarily sustained by groundwater that is recharged during snowmelt. As the climate warms over the next century, the volume of the annual Sierra Nevada snowpack is expected to decrease by ~40–90%. In eight snow‐dominated catchments in the Sierra Nevada, we analysed records of snow water equivalent (SWE) and unimpaired streamflow records spanning 10–33 years. Linear extrapolations of historical SWE/streamflow relationships suggest that annual minimum flows in some catchments could decrease to zero if peak SWE is reduced to roughly half of its historical average. For every 10% decrease in peak SWE, annual minimum flows decrease 9–22% and occur 3–7 days earlier in the year. In two of the study catchments, Sagehen and Pitman Creeks, seasonal low flows are significantly correlated with the previous year's snowpack as well as the current year's snowpack. We explore how future warming could affect the relationship between winter snowpacks and summer low flows, using a distributed hydrologic model Regional Hydro‐ecologic Ecosystem Simulation System (RHESSys) to simulate the response of two study catchments. Model results suggest that a 10% decrease in peak SWE will lead to a 1–8% decrease in low flows. The modelled streams do not dry up completely, because the effects of reduced SWE are partly offset by increased fall or winter net gains in storage, and by shifts in the timing of peak evapotranspiration. We consider how groundwater storage, snowmelt and evapotranspiration rates, and precipitation phase (snow vs rain) influence catchment response to warming. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

9.
Interannual variations in seasonal sediment transfer in two High Arctic non‐glacial watersheds were evaluated through three summers of field observations (2003–2005). Total seasonal discharge, controlled by initial watershed snow water equivalence (SWE) was the most important factor in total seasonal suspended sediment transfer. Secondary factors included melt energy, snow distribution and sediment supply. The largest pre‐melt SWE of the three years studied (2004) generated the largest seasonal runoff and disproportionately greater suspended sediment yield than the other years. In contrast, 2003 and 2005 had similar SWE and total runoff, but reduced runoff intensity resulted in lower suspended sediment concentrations and lower total suspended sediment yield in 2005. Lower air temperatures at the beginning of the snowmelt period in 2003 prolonged the melt period and increased meltwater storage within the snowpack. Subsequently, peak discharge and instantaneous suspended sediment concentrations were more intense than in the otherwise warmer 2005 season. The results for this study will aid in model development for sediment yield estimation from cold regions and will contribute to the interpretation of paleoenvironmental records obtained from sedimentary deposits in lakes. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree‐day‐based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow‐covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE‐SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

11.
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.  相似文献   

12.
Snow accumulation and melt is highly variable in space and time in complex mountainous environments. Therefore, it is necessary to provide high‐resolution spatially and temporally distributed estimates of sub‐basin snow water equivalent (SWE) to accurately predict the timing and magnitude of snowmelt runoff. In this study, we compare two reconstruction techniques (a commonly used deterministic reconstruction vs a probabilistic data assimilation framework). The methods retrospectively estimate SWE from a time series of remotely sensed maps of fractional snow‐covered area (FSCA). In testing both methods over the Tokopah watershed in the Sierra Nevada (California), the probabilistic reconstruction approach is shown to be a more robust generalization of the deterministic reconstruction. Under idealized conditions, both probabilistic and deterministic approaches perform reasonably well and yield similar results when compared with in situ verification data, whereas the probabilistic reconstruction was found to be in slightly better agreement with snow‐pit observations. More importantly, the probabilistic approach was found to be more robust: unaccounted for biases in solar radiation impacted the probabilistic SWE estimates less than the deterministic case (4% vs 7% errors for water year (WY)1997 and 0% vs 3% errors for WY1999); the probabilistic reconstruction was found to be less sensitive to the number of available observations (6% vs 10% errors in WY1997 and 13% vs 44% errors in WY1999 from the nominal cases when four fewer FSCA images were available). Finally, results from the probabilistic reconstruction approach, which requires precipitation inputs (unlike the deterministic approach), were found to be relatively robust to bias in prior precipitation estimates, where the nominal case mean estimates were recovered even when an underestimated prior precipitation was used. The additional robustness of the probabilistic SWE reconstruction technique should prove useful in future applications over larger basins and longer periods in mountainous terrain. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

13.
陆面过程模式是气候模式和天气模式的核心组成部分之一.在土壤—植被—大气耦合模式(Soil-PlantAtmosphere Model,SPAM)的基础上,发展了新一代北京大学陆面过程模式PKULM(Peking University Land Model).本文首先介绍了PKULM的辐射传输、湍流输送、光合作用、土壤水热输送等过程的参数化方案;采用隐式迭代计算框架,发展并应用了一个快速的线性方程组求解算法,提高了模式计算稳定性;提出并使用了二分搜索算法计算气孔阻抗,避免了CLM(Community Land Model)等使用的迭代方法在干旱区不稳定的情况,提高了模式的适用性;采用水势为基础的土壤水分扩散方程,使模式能够模拟土壤饱和区的水分输送过程,为进一步与水文过程模式耦合奠定了基础;还发展了一个地表积水与径流过程的机理模型,提高了模式对地表水分平衡过程的模拟能力;最后,使用"中国西北干旱区陆—气相互作用观测试验"平凉站的资料对模式进行了检验并与NOAH(National Center for Environmental Prediction,Oregon State University,Air Force,and Hydrology Lab model)陆面过程模式的模拟结果进行了比较,结果表明PKULM能够较好地模拟西北半干旱区农田下垫面地气交换过程.  相似文献   

14.
Warm winters and high precipitation in north-eastern Japan generate snow covers of more than three meters depth and densities of up to 0.55 g cm−3. Under these conditions, rain/snow ratio and snowmelt have increased significantly in the last decade under increasing warm winters. This study aims at understanding the effect of rain-on-snow and snowmelt on soil moisture under thick snow covers in mid-winter, taking into account that snowmelt in spring is an important source of water for forests and agriculture. The study combines three components of the Hydrosphere (precipitation, snow cover and soil moisture) in order to trace water mobility in winter, since soil temperatures remained positive in winter at nearly 0.3°C. The results showed that soil moisture increased after snowmelt and especially after rain-on-snow events in mid-winter 2018/2019. Rain-on-snow events were firstly buffered by fresh snow, increasing the snow water equivalent (SWE), followed by water soil infiltration once the water storage capacity of the snowpack was reached. The largest increase of soil moisture was 2.35 vol%. Early snowmelt increased soil moisture with rates between 0.02 and 0.035 vol% hr−1 while, rain-on-snow events infiltrated snow and soil faster than snowmelt and resulted in rates of up to 1.06 vol% hr−1. These results showed the strong connection of rain, snow and soil in winter and introduce possible hydrological scenarios in the forest ecosystems of the heavy snowfall regions of north-eastern Japan. Effects of rain-on-snow events and snowmelt on soil moisture were estimated for the period 2012–2018. Rain/snow ratio showed that only 30% of the total precipitation in the winter season 2011/2012 was rain events while it was 50% for the winter 2018/2019. Increasing climate warming and weakening of the Siberian winter monsoons will probably increase rain/snow ratio and the number of rain-on-snow events in the near future.  相似文献   

15.
The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day−1 resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.  相似文献   

16.
C. L. I. Ho  C. Valeo 《水文研究》2005,19(2):459-473
Urban winter hydrology has garnered very little attention owing to the general notion that high‐intensity rainfalls are the major flood‐generating events in urban areas. As a result, few efforts have been made to research urban snow and its melt characteristics. This study investigates the characteristics of urban snow that differentiate it from rural snow, and makes recommendations for incorporating these characteristics into an urban snowmelt model. A field study was conducted from the fall of 2001 to the spring of 2002 in the city of Calgary, Canada. Snow depths and densities, soil moisture, soil temperature, snow albedo, net radiation, snow evaporation, and surface temperature were measured at several locations throughout the winter period. The combination of urban snow removal practices and the physical elements that exist in urban areas were found to influence the energy balance of the snowpack profoundly. Shortwave radiation was found to be the main source of energy for urban snow; as a consequence, the albedo of urban snow is a very important factor in urban snowmelt modelling. General observations lead to the classification of snow as one of four types: snow piles, snow on road shoulders, snow on sidewalk edges, and snow in open areas. This resulted in the development of four separate functions for the changing snow albedo values. A study of the frozen ground conditions revealed that antecedent soil moisture conditions had very little impact on frozen ground, and thus frozen ground very nearly always acts as a near impervious area. Improved flood forecasting for urban catchments in cold regions can only be achieved with accurate modelling of urban winter runoff that involves the energy balance method, incorporating snow redistribution and urban snow‐cover characteristics, and using small time steps. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

17.
In the discontinuous permafrost zone of the Northwest Territories (NWT), Canada, snow covers the ground surface for half the year. Snowmelt constitutes a primary source of moisture supply for the short growing season and strongly influences stream hydrographs. Permafrost thaw has changed the landscape by increasing the proportional coverage of permafrost-free wetlands at the expense of permafrost-cored peat plateau forests. The biophysical characteristics of each feature affect snow water equivalent (SWE) accumulation and melt rates. In headwater streams in the southern Dehcho region of the NWT, snowmelt runoff has significantly increased over the past 50 years, despite no significant change in annual SWE. At the Fort Simpson A climate station, we found that SWE measurements made by Environment and Climate Change Canada using a Nipher precipitation gauge were more accurate than the Adjusted and Homogenized Canadian Climate Dataset which was derived from snow depth measurements. Here, we: (a) provide 13 years of snow survey data to demonstrate differences in end-of-season SWE between wetlands and plateau forests; (b) provide ablation stake and radiation measurements to document differences in snow melt patterns among wetlands, plateau forests, and upland forests; and (c) evaluate the potential impact of permafrost-thaw induced wetland expansion on SWE accumulation, melt, and runoff. We found that plateaus retain significantly (p < 0.01) more SWE than wetlands. However, the differences are too small (123 mm and 111 mm, respectively) to cause any substantial change in basin SWE. During the snowmelt period in 2015, wetlands were the first feature to become snow-free in mid-April, followed by plateau forests (7 days after wetlands) and upland forests (18 days after wetlands). A transition to a higher percentage cover of wetlands may lead to more rapid snowmelt and provide a more hydrologically-connected landscape, a plausible mechanism driving the observed increase in spring freshet runoff.  相似文献   

18.
Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m2 study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

19.
In this study, we simulated the snow water equivalent (SWE), rain-on-snow (ROS) events, evapotranspiration, and run-off for the period 1961–2016 in a central European region covered by low mountain ranges (<820 m a.s.l.) using a distributed hydrological model TRAnspiration and INterception evaporation model (TRAIN). We utilized improved cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products to evaluate the modelled snow-covered area, indicating a good performance of the snow modelling. We analysed the intra- and inter-annual variations of the simulated hydrological variables and the synchronous climate variables (air temperature and precipitation). Trend detection indicates a significant SWE decline throughout the snow season, but principally at the high elevations; the most severe warming occurred in early spring (March), whereas precipitation showed a slight increase in January and February. The snowpack in February has displayed the most striking reduction during the past 56 years, which is likely related to both the highest susceptibility of snow to warming and the increased ROS occurrence in February since the early 1990s. The increased combination of high temperatures and extreme rainfalls, as well as the earlier snowmelt, has resulted in a run-off increase during the earlier winter but a decrease in March. The expected changing climate towards warmer and wetter winters will probably exacerbate winter flooding in the future.  相似文献   

20.
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   

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