Glacier mass balance simulation using SWAT distributed snow algorithm

Application of a temperature-index melt model incorporated into the Soil and Water Assessment Tool (SWAT) is presented to simulate mass balance (MB) and equilibrium line altitude (ELA) of three glaciers. The snow accumulation/melt parameters were adjusted to glacierized and free glacier areas, respectively. The SWAT snow algorithm enabled us to consider spatial variation of snow parameters by elevation bands across the sub-basins, while in the previous studies using SWAT, the related parameters were constant for an entire basin. The results show slight improvement in runoff simulation and significant improvement in simulated MB when considering ELA in model calibration. The results showed that SWAT can be applied to simulate MB, vertical MB distribution and annual ELA, with light calibration efforts for data-scarce catchments. The accuracy of the results depends on the modelled area of ablation zone from which most of the meltwater is released.     

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km² mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study.     

Application of semi‐distributed hydrological model for basin level water balance of the Ken basin of Central India

In the present study, a semi‐distributed hydrological model soil and water assessment tool (SWAT) has been employed for the Ken basin of Central India to predict the water balance. The entire basin was divided into ten sub basins comprising 107 hydrological response units on the basis of unique slope, soil and land cover classes using SWAT model. Sensitivity analysis of SWAT model was performed to examine the critical input variables of the study area. For Ken basin, curve number, available water capacity, soil depth, soil evaporation compensation factor and threshold depth of water in the shallow aquifer (GWQ_MN) were found to be the most sensitive parameters. Yearly and monthly calibration (1985–1996) and validation (1997–2009) were performed using the observed discharge data of the Banda site in the Ken basin. Performance evaluation of the model was carried out using coefficient of determination, Nash–Sutcliffe efficiency, root mean square error‐observations standard deviation ratio, percent bias and index of agreement criterion. It was found that SWAT model can be successfully applied for hydrological evaluation of the Ken basin, India. The water balance analysis was carried out to evaluate water balance of the Ken basin for 25 years (1985–2009). The water balance exhibited that the average annual rainfall in the Ken basin is about 1132 mm. In this, about 23% flows out as surface run‐off, 4% as groundwater flow and about 73% as evapotranspiration. Copyright © 2013 John Wiley & Sons, Ltd.     

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

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.     

Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.     

Snow water equivalent estimation in the Mallero basin using snow gauge data and MODIS images and fieldwork validation

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.     

The comparative study of multi‐site uncertainty evaluation method based on SWAT model

With the recent development of distributed hydrological models, the use of multi‐site observed data to evaluate model performance is becoming more common. Distributed hydrological model have many advantages, and at the same time, it also faces the challenge to calibrate over‐do parameters. As a typical distributed hydrological model, problems also exist in Soil and Water Assessment Tool (SWAT) parameter calibration. In the paper, four different uncertainty approaches – Particle Swarm Optimization (PSO) techniques, Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting algorithm (SUFI‐2) and Parameter Solution (PARASOL) – are taken to a comparative study with the SWAT model applied in Peace River Basin, central Florida. In our study, the observed river discharge data used in SWAT model calibration were collected from the three gauging stations at the main tributary of the Peace River. Behind these approaches, there is a shared philosophy; all methods seek out many parameter set to fit the uncertainties due to the non‐uniqueness in model parameter evaluation. On the basis of the statistical results of four uncertainty methods, difficulty level of each method, the number of runs and theoretical basis, the reasons that affected the accuracy of simulation were analysed and compared. Furthermore, for the four uncertainty method with SWAT model in the study area, the pairwise correlation between parameters and the distributions of model fit summary statistics computed from the sampling over the behavioural parameter and the entire model calibration parameter feasible spaces were identified and examined. It provided additional insight into the relative identifiability of the four uncertainty methods Copyright © 2014 John Wiley & Sons, Ltd.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

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.     

Modelling snow water equivalent and spring runoff in a boreal watershed,James Bay,Canada

The hydrology of boreal regions is strongly influenced by seasonal snow accumulation and melt. In this study, we compare simulations of snow water equivalent (SWE) and streamflow by using the hydrological model HYDROTEL with two contrasting approaches for snow modelling: a mixed degree‐day/energy balance model (small number of inputs, but several calibration parameters needed) and the thermodynamic model CROCUS (large number of inputs, but no calibration parameter needed). The study site, in Northern Quebec, Canada was equipped with a ground‐based gamma ray sensor measuring the SWE continuously for 5 years in a small forest clearing. The first simulation of CROCUS showed a tendency to underestimate SWE, attributable to bias in the meteorological inputs. We found that it was appropriate to use a threshold of 2 °C to separate rain and snow. We also applied a correction to account for snowfall undercatch by the precipitation gauge. After these modifications to the input dataset, we noticed that CROCUS clearly overestimated the SWE, likely as a result of not including loss in SWE because of blowing snow sublimation and relocation. To correct this, we included into CROCUS a simple parameterisation effective after a certain wind speed threshold, after which the thermodynamic model performed much better than the traditional mixed degree‐day/energy balance model. HYDROTEL was then used to simulate streamflow with both snow models. With CROCUS, the main peak flow could be captured, but the second peak because of delayed snowmelt from forested areas could not be reproduced due to a lack of sub‐canopy radiation data to feed CROCUS. Despite the relative homogeneity of the boreal landscape, data inputs from each land cover type are needed to generate satisfying simulation of the spring runoff. Copyright © 2013 John Wiley & Sons, Ltd.     

Snow density variations: consequences for ground‐penetrating radar

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

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

The distribution of daily snow water equivalent in the central Italian Alps

The statistical distribution of the daily Snow Water Equivalent (SWE) is investigated for a network of gauging stations in the Alpine part of Lombardia region, in the central Italian Alps. An event based data analysis is carried out using a 14 year long data set dating back to 1989. SWE is estimated when the new snow depth is greater than 6 cm. The SWE sample average in time is shown to be related to physiographic attributes of the gauging area, thus not being homogeneous in space. The values of SWE scaled by their average, or index value, instead show well approximated homogeneity of the second order moment, or coefficient of variation, in space. This suggests the use of a regional approach for frequency estimation of SWE. The frequency of occurrence of the normalized values of SWE is evaluated and tentatively accommodated by four probability distributions, often adopted in statistical modeling of hydrological variables. The Lognormal distribution shows the best performance. Single site distribution fitting is then carried out using the regional distribution, providing satisfactory results.     

Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent

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.     

Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling

In snow-fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km² forested catchment in southern Québec, Canada (47°N, 71°W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high-resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index [LAI], tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small-scale variability across the catchment and 1810 manual snow samples were collected throughout the consecutive winters of 2016–17 and 2017–18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the LAI. We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression, binary regression tree and neural networks (NN). We were unable to establish any relationship between the CV of SWE and the LAI. However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (Nash-Sutcliffe efficiency [NSE] = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE. Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66).     

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

The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 John Wiley & Sons, Ltd.     

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

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

Parameter estimation in semi‐distributed hydrological catchment modelling using a multi‐criteria objective function

Output generated by hydrologic simulation models is traditionally calibrated and validated using split‐samples of observed time series of total water flow, measured at the drainage outlet of the river basin. Although this approach might yield an optimal set of model parameters, capable of reproducing the total flow, it has been observed that the flow components making up the total flow are often poorly reproduced. Previous research suggests that notwithstanding the underlying physical processes are often poorly mimicked through calibration of a set of parameters hydrologic models most of the time acceptably estimates the total flow. The objective of this study was to calibrate and validate a computer‐based hydrologic model with respect to the total and slow flow. The quick flow component used in this study was taken as the difference between the total and slow flow. Model calibrations were pursued on the basis of comparing the simulated output with the observed total and slow flow using qualitative (graphical) assessments and quantitative (statistical) indicators. The study was conducted using the Soil and Water Assessment Tool (SWAT) model and a 10‐year historical record (1986–1995) of the daily flow components of the Grote Nete River basin (Belgium). The data of the period 1986–1989 were used for model calibration and data of the period 1990–1995 for model validation. The predicted daily average total flow matched the observed values with a Nash–Sutcliff coefficient of 0·67 during calibration and 0·66 during validation. The Nash–Sutcliff coefficient for slow flow was 0·72 during calibration and 0·61 during validation. Analysis of high and low flows indicated that the model is unbiased. A sensitivity analysis revealed that for the modelling of the daily total flow, accurate estimation of all 10 calibration parameters in the SWAT model is justified, while for the slow flow processes only 4 out of the set of 10 parameters were identified as most sensitive. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.     

Long‐term change of seasonal snow cover and its effects on river runoff in the Tarim River basin,northwestern China

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.