Climatic control on river discharge simulations,Zackenberg River drainage basin,northeast Greenland     

Sebastian H. Mernild  Bent Hasholt  Glen E. Liston 《水文研究》2008,22(12):1932-1948

A spatially distributed, physically based, hydrologic modeling system (MIKE SHE) was applied to quantify intra‐ and inter‐annual discharge from the snow and glacierized Zackenberg River drainage basin (512 km²; 20% glacier cover) in northeast Greenland. Evolution of snow accumulation, distribution by wind‐blown snow, blowing‐snow sublimation, and snow and ice surface melt were simulated by a spatially distributed, physically based, snow‐evolution modelling system (SnowModel) and used as input to MIKE SHE. Discharge simulations were performed for three periods 1997–2001 (calibration period), 2001–2005 (validation period), and 2071–2100 (scenario period). The combination of SnowModel and MIKE SHE shows promising results; the timing and magnitude of simulated discharge were generally in accordance with observations (R² = 0·58); however, discrepancies between simulated and observed discharge hydrographs do occur (maximum daily difference up to 44·6 m³ s^?1 and up to 9% difference between observed and simulated cumulative discharge). The model does not perform well when a sudden outburst of glacial dammed water occurs, like the 2005 extreme flood event. The modelling study showed that soil processes related to yearly change in active layer depth and glacial processes (such as changes in yearly glacier area, seasonal changes in the internal glacier drainage system, and the sudden release of glacial bulk water storage) need to be determined, for example, from field studies and incorporated in the models before basin runoff can be quantified more precisely. The SnowModel and MIKE SHE model only include first‐order effects of climate change. For the period 2071–2100, future IPCC A2 and B2 climate scenarios based on the HIRHAM regional climate model and HadCM3 atmosphere–ocean general circulation model simulations indicated a mean annual Zackenberg runoff about 1·5 orders of magnitude greater (around 650 mmWE year^?1) than from today 1997–2005 (around 430 mmWE year^?1), mainly based on changes in negative glacier net mass balance. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Quantitative evaluation of different hydrological modelling approaches in a partly glacierized Swiss watershed     

Jan Magnusson  Daniel Farinotti  Tobias Jonas  Mathias Bavay 《水文研究》2011,25(13):2071-2084

Mountain water resources management often requires hydrological models that need to handle both snow and ice melt. In this study, we compared two different model types for a partly glacierized watershed in central Switzerland: (1) an energy‐balance model primarily designed for snow simulations; and (2) a temperature‐index model developed for glacier simulations. The models were forced with data extrapolated from long‐term measurement records to mimic the typical input data situation for climate change assessments. By using different methods to distribute precipitation, we also assessed how various snow cover patterns influenced the modelled runoff. The energy‐balance model provided accurate discharge estimations during periods dominated by snow melt, but dropped in performance during the glacier ablation season. The glacier melt rates were sensitive to the modelled snow cover patterns and to the parameterization of turbulent heat fluxes. In contrast, the temperature‐index model poorly reproduced snow melt runoff, but provided accurate discharge estimations during the periods dominated by glacier ablation, almost independently of the method used to distribute precipitation. Apparently, the calibration of this model compensated for the inaccurate precipitation input with biased parameters. Our results show that accurate estimates of snow cover patterns are needed either to correctly constrain the melt parameters of the temperature‐index model or to ensure appropriate glacier surface albedos required by the energy‐balance model. Thus, particularly when only distant meteorological stations are available, carefully selected input data and efficient extrapolation methods of meteorological variables improve the reliability of runoff simulations in high alpine watersheds. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

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

Andras J. Szeitz  R. Dan Moore 《水文研究》2023,37(1):e14800

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%.  相似文献   

Snow-distribution and melt modelling for glaciers in Zackenberg river drainage basin,north-eastern Greenland     

Sebastian H. Mernild  Glen E. Liston  Bent Hasholt 《水文研究》2007,21(24):3249-3263

A physically based snow-evolution modelling system (SnowModel) that includes four sub-models: MicroMet, EnBal, SnowPack, and SnowTran-3D, was used to simulate eight full-year evolutions of snow accumulation, distribution, sublimation, and surface melt from glaciers in the Zackenberg river drainage basin, in north-east Greenland. Meteorological observations from two meteorological stations were used as model inputs, and spatial snow depth observations, snow melt depletion curves from photographic time lapse, and a satellite image were used for model testing of snow and melt simulations, which differ from previous SnowModel tests methods used on Greenland glaciers. Modelled test-period-average end-of-winter snow water equivalent (SWE) depth for the depletion area differs by a maximum of 14 mm w.eq., or ∼6%, more than the observed, and modelled test-period-average snow cover extent differs by a maximum of 5%, or 0·8 km², less than the observed. Furthermore, comparison with a satellite image indicated a 7% discrepancy between observed and modelled snow cover extent for the entire drainage basin. About 18% (31 mm w.eq.) of the solid precipitation was returned to the atmosphere by sublimation. Modelled mean annual snow melt and glacier ice melt for the glaciers in the Zackenberg river drainage basin from 1997 through 2005 (September–August) averaged 207 mm w.eq. year⁻¹ and 1198 mm w.eq. year⁻¹, respectively, yielding a total averaging 1405 mm w.eq. year⁻¹. Total modelled mean annual surface melt varied from 960 mm w.eq. year⁻¹ to 1989 mm w.eq. year⁻¹. The surface-melt period started between mid-May and the beginning of June and lasted until mid-September. Annual calculated runoff averaged 1487 mm w.eq. year⁻¹ (∼150 × 10⁶ m³) (1997–2005) with variations from 1031 mm w.eq. year⁻¹ to 2051 mm w.eq. year⁻¹. The model simulated a total glacier recession averaging − 1347 mm w.eq. year⁻¹ (∼136 × 10⁶ m³) (1997–2005), which was almost equal to previous basin average hydrological water balance storage studies − 244 mm w.eq. year⁻¹ (∼125 × 10⁶ m³) (1997–2003). Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements     

Siwei He  Noriaki Ohara  Scott N. Miller 《水文研究》2019,33(11):1525-1537

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   

Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkaido,Japan     

Kazuyoshi Suzuki  Yuji Kodama  Taro Nakai  Glen E. Liston  Kazukiyo Yamamoto  Tetsuo Ohata 《水文科学杂志》2013,58(3):443-467

Abstract

We simulated snow processes in a forested region with heavy snowfall in Japan, and evaluated both the regional-scale snow distribution and the potential impact of land-use changes on the snow cover and water balances over the entire domain. SnowModel reproduced the snow processes at open and forested sites, which were confirmed by snow water equivalent (SWE) measurements at two intensive observation sites and snow depth measurements at the Automated Meteorological Data Acquisition System sites. SnowModel also reproduced the observed snow distribution (from the MODIS snow cover data) over the simulation domain during thaw. The observed SWE was less at the forested site than at the open site. The SnowModel simulations showed that this difference was caused mainly by differences in sublimation. The type of land use changed the maximum SWE, onset and duration of snowmelt, and the daily snowmelt rate due to canopy snow interception.

Citation Suzuki, K., Kodama, Y., Nakai, T., Liston, G. E., Yamamoto, K., Ohata, T., Ishii, Y., Sumida, A., Hara, T. & Ohta, T. (2011) Impact of land-use changes in a forested region with heavy snowfall in Hokkaido, Japan. Hydrol. Sci. J. 56(3), 443–467.  相似文献   

A modified monthly degree‐day model for evaluating glacier runoff changes in China. Part I: model development     

Shiqiang Zhang  Baisheng Ye  Shiyin Liu  Xiaowen Zhang  Stefan Hagemann 《水文研究》2012,26(11):1686-1696

The retreat of mountain glaciers and ice caps has dominated the rise in global sea level and is likely to remain an import component of eustatic sea‐level rise in the 21st century. Mountain glaciers are critical in supplying freshwater to populations inhabiting the valleys downstream who heavily rely on glacier runoff, such as arid and semi‐arid regions of western China. Owing to recent climate warming and the consequent rapid retreat of many glaciers, it is essential to evaluate the long‐term change in glacier melt water production, especially when considering the glacier area change. This paper describes the structure, principles and parameters of a modified monthly degree‐day model considering glacier area variation. Water balances in different elevation bands are calculated with full consideration of the monthly precipitation gradient and air temperature lapse rate. The degree‐day factors for ice and snow are tuned by comparing simulated variables to observation data for the same period, such as mass balance, equilibrium line altitude and glacier runoff depth. The glacier area–volume scaling factor is calibrated with the observed glacier area change monitored by remote sensing data of seven sub‐basins of the Tarim interior basin. Based on meteorological data, the glacier area, mass balance and runoff are estimated. The model can be used to evaluate the long‐term changes of melt water in all glacierized basins of western China, especially for those with limited observation data. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment          下载免费PDF全文

Michael Engel  Claudia Notarnicola  Stefano Endrizzi  Giacomo Bertoldi 《水文研究》2017,31(23):4151-4168

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.  相似文献   

Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland     

《Advances in water resources》2013

A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021–2050 and 2070–2095 periods from an ensemble of regional climate models.The predicted changes in snow cover will be moderate for 2021–2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.  相似文献   

Determining annual cryosphere storage contributions to streamflow using historical hydrometric records          下载免费PDF全文

Janice Brahney  Brian Menounos  Xiaohua Wei  Paul Jefferson Curtis 《水文研究》2017,31(8):1590-1601

Alpine glaciers and perennial snow fields are important hydrologic elements in many mountain environments providing runoff during the late summer and during periods of drought. Because relatively long records of glacier mass–balance data are absent from many glacierized catchments, it remains unclear to what extent shrinking perennial snow and glaciers have affected runoff trends from these watersheds. Here, we employ a hydrograph separation technique that uses a double mass curve in an attempt to isolate changes in runoff due to glacier retreat and disappearance of perennial snow. The method is tested using hydrometric data from 20 glacierized and 16 nonglacierized catchments in the Columbia Basin of Canada. The resulting estimates on cryosphere storage contribution to streamflow were well correlated to other regional estimates on the basis of measurements as well as empirical and mechanistic models. Annual cryosphere runoff changed from +19 to ?55% during the period 1975–2012, with an average decline of 26%. For August runoff, these changes ranged from +17 to ?66%, with an average decrease of 24%. Reduction of cryosphere contributions to annual and late summer flows is expected to continue in the coming decades as glaciers and the perennial snow patches shrink. Our method to isolate changes in late summer cryospheric storage contributions can be used as a first order estimate on changes in glacier contributions to flow and may help researchers and water managers target watersheds for further analysis.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Modeling the runoff and glacier mass balance in a small watershed on the Central Tibetan Plateau,China, from 1955 to 2008   总被引：1，自引：0，他引：1  

Hongkai Gao  Xiaobo He  Baisheng Ye  Jianchen Pu 《水文研究》2012,26(11):1593-1603

The glaciers on Tibetan Plateau play an important role in the catchment hydrology of this region. However, our knowledge with respect to water circulation in this remote area is scarce. In this study, the HBV light model, which adopts the degree‐day model for glacial melting, was employed to simulate the total runoff, the glacier runoff and glacier mass balance (GMB) of the Dongkemadi River Basin (DRB) at the headwater of the Yangtze River on the Tibetan Plateau, China. Firstly, the daily temperature and precipitation of the DRB from 1955 to 2008 were obtained by statistical methods, based on daily meteorological data observed in the DRB (2005–2008) and recorded by four national meteorological stations near the DRB (1955–2008). Secondly, we used 4‐year daily air temperature, precipitation, runoff depth and monthly evaporation, which were observed in the DRB, as input to obtain a set of proper parameters. Then, the annual runoff, the glacier runoff and GMB (1955–2008) were calculated using the HBV model driven by interpolated meteorological data. The calculated GMB fits well with the observed results. At last, using the temperature and precipitation predicted by climate models, we predicted the changes of runoff depth and GMB of the DRB in the next 40 years. Under all climate‐change scenarios, annual glacier runoff shows a significant increase due to intensified ice melting. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Data time step to estimate snowpack accumulation at select United States meteorological stations     

S. R. Fassnacht 《水文研究》2007,21(12):1608-1615

When estimating the water balance for a cold region watershed, that is one that receive a substantial portion of its annual precipitation as snow, accumulation and other winter hydrological processes must be considered. For many of theses watersheds, all but the most fundamental meteorological data (temperature and precipitation), are either not measured or not measured at a reasonable time step. Of particular importance are wind data, as wind influences underestimates of precipitation due to wind undercatch and losses of snow from the snowpack, specifically, snowpack sublimation, and the occurrence and magnitude of blowing snow. Estimating snow accumulation to yield snowmelt amounts requires summing of gauged precipitation and gauge undercatch, and subtracting minus snowpack sublimation and blowing snow transport. The first two components are computed on a daily time step, while the latter two are computed on an hourly time step. From five National Weather Service meteorological stations (Pullman WA, Rawlins WY, Leadville CO, Rhinelander WI, Syracuse NY), the variations in computed snowpack mass losses minus undercatch using data at different time intervals show that at most sites it is difficult to use monthly time steps for computations derived using hourly or daily data. At the relative dry and cold Leadville, Colorado site the computations were transferable between time steps. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Empirical mass balance modelling of South American tropical glaciers: case study of Antisana volcano,Ecuador     

Carla Manciati  Marcos Villacís  Jean-Denis Taupin  Eric Cadier  Remigio Galárraga-Sánchez  Bolívar Cáceres 《水文科学杂志》2013,58(8):1519-1535

Abstract

Most Latin American glaciers are located in the tropical Andes. The melting processes of Glacier “15” on Antisana volcano were studied to understand the relationship between glacier retreat and natural climate variability and global climate change. Glaciers on the Antisana volcano are crucial sources of water as they feed the headwater rivers that supply Quito with potable water. The aim of this study was to build empirical models based on multiple correlations to reconstruct the mass loss of glaciers over a period of 10 years at three scales: local (data recorded by meteorological stations located around the volcano), regional (data from stations located around the country) and global (re-analysis data). Data quality was checked using graphical and statistical methods. Several empirical models based on multiple correlations were created to generate longer time series (42 and 115 years) of the mass balance for the glacier ablation zone. The long mass balance series were compared with the temperature variation series of the Earth’s surface in the Southern Hemisphere to estimate the relation between the mass balance and global warming. Our results suggest that the meteorological factors that best correlate with mass balance are temperature and wind.