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1.
High‐resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km2) in the Pyrenees were used to assess small‐scale variability of the snowpack at the catchment and sub‐grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small‐scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small‐scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub‐grid variability. Topographic variables including slope, wind sheltering, sub‐grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small‐scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

2.
We report a methodology for reconstructing the daily snow depth distribution at high spatial resolution in a small Pyrenean catchment using time‐lapse photographs and snow depletion rates derived from an on‐site measuring meteorological station. The results were compared with the observed snow depth distribution, determined on a number of separate occasions using a terrestrial laser scanner (TLS). The time‐lapse photographs were projected onto a digital elevation model of the study site, and converted into snow presence/absence information. The melt‐out date (MOD; first occurrence of melt out after peak snow accumulation) was obtained from the projected photograph series. Commencing the backward reconstruction for each grid cell at the MOD, the method uses simulated snow depth depletion rates using a temperature index approach, which are extrapolated to the grid cells of the domain to arrive at the snow distribution of the previous day. Two variants of the reconstruction techniques were applied (1) using a spatially constant degree day factor (DDF) for calculating the daily expected snow depth depletion rate, and (2) allowing a spatially distributed DDF calculated from two consecutive TLS acquisitions compared to the snow depth depletion rate observed at the meteorological station. Validation revealed that both methods performed well (average R2 = 0.68; standard RMSE = 0.58), with better results obtained from the spatially distributed approach. Nevertheless, the spatially corrected DDF reconstruction, which requires TLS data, suggests that the constant DDF approach is an efficient, and for most applications sufficiently accurate and easily reproducible method. The results highlight the usefulness of time‐lapse photography for not only determining the snow covered area, but also for estimating the spatial distribution of snow depth. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

3.
A method for using remotely sensed snow cover information in updating a hydrological model is developed, based on Bayes' theorem. A snow cover mass balance model structure adapted to such use of satellite data is specified, using a parametric snow depletion curve in each spatial unit to describe the subunit variability in snow storage. The snow depletion curve relates the accumulated melt depth to snow‐covered area, accumulated snowmelt runoff volume, and remaining snow water equivalent. The parametric formulation enables updating of the complete snow depletion curve, including mass balance, by satellite data on snow coverage. Each spatial unit (i.e. grid cell) in the model maintains a specific depletion curve state that is updated independently. The uncertainty associated with the variables involved is formulated in terms of a joint distribution, from which the joint expectancy (mean value) represents the model state. The Bayesian updating modifies the prior (pre‐update) joint distribution into a posterior, and the posterior joint expectancy replaces the prior as the current model state. Three updating experiments are run in a 2400 km2 mountainous region in Jotunheimen, central Norway (61°N, 9°E) using two Landsat 7 ETM+ images separately and together. At 1 km grid scale in this alpine terrain, three parameters are needed in the snow depletion curve. Despite the small amount of measured information compared with the dimensionality of the updated parameter vector, updating reduces uncertainty substantially for some state variables and parameters. Parameter adjustments resulting from using each image separately differ, but are positively correlated. For all variables, uncertainty reduction is larger with two images used in conjunction than with any single image. Where the observation is in strong conflict with the prior estimate, increased uncertainty may occur, indicating that prior uncertainty may have been underestimated. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

4.
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   

5.
The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R2 values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.  相似文献   

6.
A generalized additive model (GAM) was used to model the spatial distribution of snow depth in the central Spanish Pyrenees. Statistically significant non‐linear relationships were found between distinct location and topographical variables and the average depth of the April snowpack at 76 snow poles from 1985 to 2000. The joint effect of the predictor variables explained more than 73% of the variance of the dependent variable. The performance of the model was assessed by applying a number of quantitative approaches to the residuals from a cross‐validation test. The relatively low estimated errors and the possibility of understanding the processes that control snow accumulation, through the response curves of each independent variable, indicate that GAMs may be a useful tool for interpolating local snow depth or other climate parameters. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

7.
The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

8.
It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.  相似文献   

9.
Ground‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.  相似文献   

10.
Multivariate statistical analysis was used to explore relationships between catchment topography and spatial variability in snow accumulation and melt processes in a small headwater catchment in the Spanish Pyrenees. Manual surveys of snow depth and density provided information on the spatial distribution of snow water equivalent (SWE) and its depletion over the course of the 1997 and 1998 melt seasons. A number of indices expressing the topographic control on snow processes were extracted from a detailed digital elevation model of the catchment. Bivariate screening was used to assess the relative importance of these topographic indices in controlling snow accumulation at the start of the melt season, average melt rates and the timing of snow disappearance. This suggested that topographic controls on the redistribution of snow by wind are the most important influence on snow distribution at the start of the melt season. Furthermore, it appeared that spatial patterns of snow disappearance were largely determined by the distribution of snow water equivalent (SWE) at the start of the melt season, rather than by spatial variability in melt rates during the melt season. Binary regression tree models relating snow depth and disappearance date to terrain indices were then constructed. These explained 70–80% of the variance in the observed data. As well as providing insights into the influence of topography on snow processes, it is suggested that the techniques presented herein could be used in the parameterization of distributed snowmelt models, or in the design of efficient stratified snow surveys. Copyright © 2003 John Wiley & Sons, Ltd.  相似文献   

11.
We investigate the problem of balancing model complexity and input data requirements in snow hydrology. For this purpose, we analyze the performance of two models of different complexity in estimating variables of interest in snow hydrology applications. These are snow depth, bulk snow density, snow water equivalent and snowmelt run‐off. We quantify the differences between data and model prediction using 18 years of measurements from an experimental site in the French Alps (Col de Porte, 1325 m AMSL). The models involved in this comparison are a one‐layer temperature‐index model (HyS) and a multilayer model (Crocus). Results show that the expected loss in performance in the one‐layer temperature‐index model with respect to the multilayer model is low when considering snow depth, snow water equivalent and bulk snow density. As for run‐off, the comparison returns less clear indications for identification of a balance. In particular, differences between the models' prediction and data with an hourly resolution are higher when considering the Crocus model than the HyS model. However, Crocus is better at reproducing sub‐daily cycles in this variable. In terms of daily run‐off, the multilayer physically based model seems to be a better choice, while results in terms of cumulative run‐off are comparable. The better reproduction of daily and sub‐daily variability of run‐off suggests that use of the multilayer model may be preferable for this purpose. Variation in performance is discussed as a function of both the calibration solution chosen and the time of year. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

12.
This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2 km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.  相似文献   

13.
Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well as on the fate of pollutants. Therefore, snow and frost need to be central inherent elements of risk assessment and management schemes. However, snow and frost are often neglected or treated summarily or in a simplistic way by groundwater modellers. Snow deposition is uneven, and the snow is likely to sublimate, be redistributed and partly melt during the winter influencing the mass and spatial distribution of snow storage available for infiltration, the presence of ice layers within and under the snowpack and, therefore, also the spatial distribution of depths and permeability of the soil frost. In steep terrain, snowmelt may travel downhill tens of metres in hours along snow layers. The permeability of frozen soil is mainly influenced by soil type, its water and organic matter content, and the timing of the first snow in relation to the timing of sub‐zero temperatures. The aim with this paper is to review the literature on snow and frost processes, modelling approaches with the purpose to visualize and emphasize the need to include these processes when modelling, managing and predicting groundwater recharge for areas exposed to seasonal snow and frost. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

14.
In this study, we compare gridded snow depth estimates from the Snow Data Assimilation System (SNODAS) with snow depth observations derived from GPS interferometric reflectometry (GPS‐IR) from roughly 100 Plate Boundary Observatory sites in the Western United States spanning four water‐years (2010–2013). Data from these sites are not assimilated by SNODAS; thus, GPS‐IR measurements provide an independent data set to evaluate SNODAS. Our results indicate that at 80% of the sites, SNODAS and GPS‐IR snow depth agree to better than 15‐cm root mean square error, with correlation coefficients greater than 0.6. Significant differences are found between GPS‐IR and SNODAS for sites that are distant from other point measurements, are located in complex terrain or are in areas with strong vegetation heterogeneities. GPS‐IR estimates of snow depth are shown to provide useful error characterization of SNODAS products across much of the Western United States and may have potential as an additional data assimilation source that could help improve SNODAS estimates. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.  相似文献   

15.
This paper reports on a study analysing the spatial distribution functions, the correlation structures, and the power spectral densities of high‐resolution LIDAR snow depths (～1 m) in two adjacent 500 m × 500 m areas in the Colorado Rocky Mountains, one a sub‐alpine forest the other an alpine tundra. It is shown how and why differences in the controlling physical processes induced by variations in vegetation cover and wind patterns lead to the observed differences in spatial organization between the snow depth fields of these environments. In the sub‐alpine forest area, the mean of snow depth increases with elevation, while its standard deviation remains uniform. In the tundra subarea, the mean of snow depth decreases with elevation, while its standard deviation varies over a wide range. The two‐dimensional correlations of snow depth in the forested area indicate little spatial memory and isotropic conditions, while in the tundra they indicate a marked directional bias that is consistent with the predominant wind directions and the location of topographic ridges and depressions. The power spectral densities exhibit a power law behaviour in two frequency intervals separated by a break located at a scale of around 12 m in the forested subarea, and 65 m in the tundra subarea. The spectral exponents obtained indicate that the snow depth fields are highly variable over scales larger than the scale break, while highly correlated below. Based on the observations and on synthetic snow depth fields generated with one‐ and two‐dimensional spectral techniques, it is shown that the scale at which the break occurs increases with the separation distance between snow depth maxima. In addition, the breaks in the forested area coincide with those of the corresponding vegetation height field, while in the tundra subarea they are displaced towards larger scales than those observed in the corresponding vegetation height field. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

16.
The spatio‐temporal distribution of snow in a catchment during ablation reflects changes in the total amount of snow water equivalent and is thus a key parameter for the estimation of melt water run‐off. This study explores possible rules behind the spatial variability of snow depth during the ablation season in a small Alpine catchment with complex topography. The snow depth observations are based on more than 160 000 terrestrial laser scanner data points with a spatial resolution of 1 m, which were obtained from 11 scanning campaigns of two consecutive ablation seasons. The analysis suggests that for estimating cumulative snow melt dynamics from the catchment investigated, assessing the initial snow distribution prior to the melt season is more important than addressing spatial differences in the melt behaviour. Snow volume and snow‐covered area could be predicted well using a conceptual melt model assuming spatially uniform melt rates. However, accurate results were only obtained if the model was initialized with a pre‐melt snow distribution that reflected measured mean and standard deviation. Using stratified melt rates on the other hand did not improve the model results. At least for sites with similar meteorological and topographical conditions, the model approach presented here comprises an efficient way to estimate snow depletion dynamics, especially if persistent snow accumulation pattern between years facilitate the characterization of the initial snow distribution prior to the melt. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

17.
An analysis of the hydrological effects of vegetation changes in the Columbia River basin over the last century was performed using two land cover scenarios. The first was a reconstruction of historical land cover vegetation, c. 1900, as estimated by the federal Interior Columbia Basin Ecosystem Management Project (ICBEMP). The second was current land cover as estimated from remote sensing data for 1990. Simulations were performed using the variable infiltration capacity (VIC) hydrological model, applied at one‐quarter degree spatial resolution (approximately 500 km2 grid cell area) using hydrometeorological data for a 10 year period starting in 1979, and the 1900 and current vegetation scenarios. The model represents surface hydrological fluxes and state variables, including snow accumulation and ablation, evapotranspiration, soil moisture and runoff production. Simulated daily hydrographs of naturalized streamflow (reservoir effects removed) were aggregated to monthly totals and compared for nine selected sub‐basins. The results show that, hydrologically, the most important vegetation‐related change has been a general tendency towards decreased vegetation maturity in the forested areas of the basin. This general trend represents a balance between the effects of logging and fire suppression. In those areas where forest maturity has been reduced as a result of logging, wintertime maximum snow accumulations, and hence snow available for runoff during the spring melt season, have tended to increase, and evapotranspiration has decreased. The reverse has occurred in areas where fire suppression has tended to increase vegetation maturity, although the logging effect appears to dominate for most of the sub‐basins evaluated. Predicted streamflow changes were largest in the Mica and Corralin sub‐basins in the northern and eastern headwaters region; in the Priest Rapids sub‐basin, which drains the east slopes of the Cascade Mountains; and in the Ice Harbor sub‐basin, which receives flows primarily from the Salmon and Clearwater Rivers of Idaho and western Montana. For these sub‐basins, annual average increases in runoff ranged from 4·2 to 10·7% and decreases in evapotranspiration ranged from 3·1 to 12·1%. In comparison with previous studies of individual, smaller sized watersheds, the modelling approach used in this study provides predictions of hydrological fluxes that are spatially continuous throughout the interior Columbia River basin. It thus provides a broad‐scale framework for assessing the vulnerability of watersheds to altered streamflow regimes attributable to changes in land cover that occur over large geographical areas and long time‐frames. Copyright © 2000 John Wiley & Sons, Ltd.  相似文献   

18.
Direct measurements of winter water loss due to sublimation were made in a sub‐alpine forest in the Rocky Mountains of Colorado. Above‐and below‐canopy eddy covariance systems indicated substantial losses of winter‐season snow accumulation in the form of snowpack (0·41 mm d?1) and intercepted snow (0·71 mm d?1) sublimation. The partitioning between these over and under story components of water loss was highly dependent on atmospheric conditions and near‐surface conditions at and below the snow/atmosphere interface. High above‐canopy sensible heat fluxes lead to strong temperature gradients between vegetation and the snow‐surface, driving substantial specific humidity gradients at the snow surface and high sublimation rates. Intercepted snowfall resulted in rapid response of above‐canopy latent heat fluxes, high within‐canopy sublimation rates (maximum = 3·7 mm d?1), and diminished sub‐canopy snowpack sublimation. These results indicate that sublimation losses from the sub‐canopy snowpack are strongly dependent on the partitioning of sensible and latent heat fluxes in the canopy. This compels comprehensive studies of snow sublimation in forested regions that integrate sub‐canopy and over‐story processes. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

19.
Seasonal snow is a globally important water resource that contributes substantially to upland and lowland water resources. As such, there is a need to understand the controls on the spatial and temporal variation in snow distribution. This study meets this research need by investigating the topographic controls on snow depth distribution in the upper Jollie catchment in the Southern Alps of New Zealand. Furthermore, inter‐annual variation in the importance of the topographic controls is examined and linked to variation in the dominant synoptic‐scale weather patterns over a 4‐year period (2007–2010). Through the use of regression trees, the relative importance of the topographic controls on snow depth was shown to vary between the four study years. In particular, elevation explained the greatest amount of variance in 2007 and 2008 and east‐exposure explained the greatest variance in 2009 and 2010. The other wind exposure variables also had a large effect on the snow depth distribution in 2009 and 2010. Differences in the frequency and duration of synoptic weather patterns were physically consistent with the changing importance of these variables. In particular, a higher frequency of troughing events in 2009 and 2010 is thought to be associated with a reduced importance of elevation and greater influence of wind exposure on snow depth in these years. These findings demonstrate the importance of using multi‐year data sets, and of considering topographic and climatic influences, when attempting to model alpine snow distribution. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

20.
Spatio‐temporal variation of snow depth in the Tarim River basin has been studied by the empirical orthogonal function (EOF) based on the data collected by special sensor microwave/imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) during the period from 1979 to 2005. The long‐term trend of snow depth and runoff was presented using the Mann‐Kendall non‐parametric test, and the effects of the variations of snow depth and climatic factors on runoff were analysed and discussed by means of the regression analysis. The results suggested that the snow depth variation on the entire basin was characterised by four patterns: all consistency, north–south contrast, north‐middle‐south contrast and complex. The first pattern accounting 39·13% of the total variance was dominant. The entire basin was mainly affected by one large‐scale weather system. However, the spatial and temporal differences also existed among the different regions in the basin. The significant snow depth changes occurred mainly in the Aksu River basin with the below‐normal snow depth anomalies in the 1980s and the above‐normal snow depth anomalies in the 1990s. The long‐term trend of snow depth was significant in the northwestern, western and southern parts of the basin, whereas the long‐term trend of runoff was significant in the northwestern and northeastern parts. The regression analysis revealed that the runoff of the rivers replenished by snow melt water and rainfall was related primarily to the summer precipitation, followed by the summer temperature or the maximum snow depth in the cold season. Our results suggest that snow is not the principal factor that contributes to the runoff increase in headstreams, although there was a slow increase in snow depth. It is the climatic factors that are responsible for the steady and continuous water increase in the headstreams. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

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