scholarly journals Spatiotemporal Variations in Snow Cover and Hydrological Effects in the Upstream Region of the Shule River Catchment, Northwestern China

2021 ◽  
Vol 13 (16) ◽  
pp. 3212
Author(s):  
Youyan Jiang ◽  
Wentao Du ◽  
Jizu Chen ◽  
Wenxuan Sun
Keyword(s):  
Snow Cover ◽  
Global Climate ◽  
Snow Accumulation ◽  
Upstream Region ◽  
Slope Aspect ◽  
Spatiotemporal Variations ◽  
River Catchment ◽  
Ice Melt ◽  
Inland River ◽  
Snow Cover Extent

Precipitation and snow/ice melt water are the primary water sources in inland river basins in arid areas, and these are sensitive to global climate change. A dataset of snow cover in the upstream region of the Shule River catchment was established using MOD10A2 data from 2000 to 2019, and the spatiotemporal variations in the snow cover and its meteorological, runoff, and topographic impacts were analyzed. The results show that the spatial distribution of the snow cover is highly uneven owing to altitude differences. The snow cover in spring and autumn is mainly concentrated along the edges of the region, whereas that in winter and summer is mainly distributed in the south. Notable differences in snow accumulation and melting are observed at different altitudes, and the annual variation in the snow cover extent shows bimodal characteristics. The correlation between the snow cover extent and runoff is most significant in April. The snow cover effectively replenishes the runoff at higher altitudes (3300–4900 m), but this contribution weakens with increasing altitude (>4900 m). The regions with a high snow cover frequency are mostly concentrated at high altitudes. Regions with slopes of <30° show a strong correlation with the snow cover frequency, which decreases for slopes of >45°. The snow cover frequency and slope aspect show symmetrical changes.

scholarly journals Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

2013 ◽  
Vol 17 (10) ◽  
pp. 3921-3936 ◽  
Author(s):  
M. Ménégoz ◽  
H. Gallée ◽  
H. W. Jacobi
Keyword(s):  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
The Tibetan Plateau ◽  
Monsoon Period ◽  
Medium Range Weather Forecast ◽  
Snow Cover Extent ◽  
The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

A Physically Based Daily Hydrometeorological Model for Complex Mountain Terrain

2009 ◽  
Vol 10 (6) ◽  
pp. 1430-1446 ◽  
Author(s):  
Ryan J. MacDonald ◽  
James M. Byrne ◽  
Stefan W. Kienzle
Keyword(s):  
Snow Cover ◽  
Estimation Method ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Effect Of Temperature ◽  
Systems Science ◽  
River Watershed ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent ◽  
Physically Based

Abstract This paper describes the continued development of the physically based hydrometeorological model Generate Earth Systems Science input (GENESYS) and its application in simulating snowpack in the St. Mary (STM) River watershed, Montana. GENESYS is designed to operate a high spatial and temporal resolution over complex mountainous terrain. The intent of this paper is to assess the performance of the model in simulating daily snowpack and the spatial extent of snow cover over the St. Mary River watershed. A new precipitation estimation method that uses snowpack telemetry (SNOTEL) and snow survey data is presented and compared with two other methods, including Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation data. A method for determining daily temperature lapse rates from NCEP reanalysis data is also presented and the effect of temperature lapse rate on snowpack simulations is determined. Simulated daily snowpack values compare well with observed values at the Many Glacier SNOTEL site, with varying degrees of accuracy, dependent on the method used to estimate precipitation. The spatial snow cover extent compares well with Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products for three dates selected to represent snow accumulation and ablation periods.

scholarly journals Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

2017 ◽  
Vol 9 (2) ◽  
pp. 765-777 ◽  
Author(s):  
George A. Riggs ◽  
Dorothy K. Hall ◽  
Miguel O. Román
Keyword(s):  
Snow Cover ◽  
Information Content ◽  
Infrared Imaging ◽  
Global Climate ◽  
Detection Accuracy ◽  
Earth System ◽  
Detection Algorithms ◽  
Snow Cover Extent ◽  
Data Products ◽  
System Data

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

2012 ◽  
Vol 6 (6) ◽  
pp. 4637-4671
Author(s):  
K. Klehmet ◽  
B. Geyer ◽  
B. Rockel
Keyword(s):  
Regional Climate Model ◽  
Snow Cover ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Snow Accumulation ◽  
Added Value ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an alpine watershed

2010 ◽  
Vol 7 (3) ◽  
pp. 3189-3211 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Bias Error ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote sensing data were used to simulate snow distribution and melt in an alpine watershed in Northwestern China within a complete snow accumulation-melt period. Spatial energy budgets were simulated using the meteorological observations and digital elevation model of the watershed. A linear interpolation method was used to discriminate daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer data. Hourly snow distribution and melt, snow cover extent, and daily discharge were included in the simulated results. The bias error between field snow water equivalent samplings and simulated results is −2.1 cm, and Root Mean Square Error is 33.9 cm. The Nash and Sutcliffe efficiency statistic (R2) between measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9%. Using the method introduced in this article, modeling spatial snow distribution and melt runoff will become relatively convenient.

scholarly journals Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

2013 ◽  
Vol 10 (6) ◽  
pp. 7651-7686 ◽  
Author(s):  
M. Ménégoz ◽  
H. Gallée ◽  
H. W. Jacobi
Keyword(s):  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Western Himalaya ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
The Tibetan Plateau ◽  
Rain Gauges ◽  
Snow Cover Extent ◽  
The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 to December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis used as boundary conditions. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its Eastern part. However, our simulation brings an interesting estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are few reliable. We found our model to simulate quite accurately the snow cover extent and duration for the two years of simulation in these areas. Snow accumulation and snow duration differ widely along the Himalaya: snowfall can occur during the whole year Western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan plateau, a much more marked dry season occurs from October to March. Snow cover does not have a well marked seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the winter.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed

2011 ◽  
Vol 15 (7) ◽  
pp. 2195-2203 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE) between the measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

scholarly journals Snowcover Survey over an Arctic Glacier Forefield: Contribution of Photogrammetry to Identify “Icing” Variability and Processes

2021 ◽  
Vol 13 (10) ◽  
pp. 1978
Author(s):  
Éric Bernard ◽  
Jean-Michel Friedt ◽  
Madeleine Griselin
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Measurement Techniques ◽  
Snow Accumulation ◽  
Surface Model ◽  
Bare Rock ◽  
Glacier Forefield ◽  
Arctic Glacier

The global climate shift currently underway has significant impacts on both the quality and quantity of snow precipitation. This directly influences the spatial variability of the snowpack as well as cumulative snow height. Contemporary glacier retreat reorganizes periglacial morphology: while the glacier area decreases, the moraine area increases. The latter is becoming a new water storage potential that is almost as important as the glacier itself, but with considerably more complex topography. Hence, this work fills one of the missing variables of the hydrological budget equation of an arctic glacier basin by providing an estimate of the snow water equivalent (SWE) of the moraine contribution. Such a result is achieved by investigating Structure from Motion (SfM) image processing that is applied to pictures collected from an Unmanned Aerial Vehicle (UAV) as a method for producing snow depth maps over the proglacial moraine area. Several UAV campaigns were carried out on a small glacial basin in Spitsbergen (Arctic): the measurements were made at the maximum snow accumulation season (late April), while the reference topography maps were acquired at the end of the hydrological year (late September) when the moraine is mostly free of snow. The snow depth is determined from Digital Surface Model (DSM) subtraction. Utilizing dedicated and natural ground control points for relative positioning of the DSMs, the relative DSM georeferencing with sub-meter accuracy removes the main source of uncertainty when assessing snow depth. For areas where snow is deposited on bare rock surfaces, the correlation between avalanche probe in-situ snow depth measurements and DSM differences is excellent. Differences in ice covered areas between the two measurement techniques are attributed to the different quantities measured: while the former only measures snow accumulation, the latter includes all of the ice accumulation during winter through which the probe cannot penetrate, in addition to the snow cover. When such inconsistencies are observed, icing thicknesses are the source of the discrepancy that is observed between avalanche probe snow cover depth measurements and differences of DSMs.

scholarly journals Contribution of Changes in Snow Cover Extent to Shortwave Radiation Perturbations at the Top of the Atmosphere over the Northern Hemisphere during 2000–2019

2021 ◽  
Vol 13 (23) ◽  
pp. 4938
Author(s):  
Xiaona Chen ◽  
Yaping Yang ◽  
Cong Yin
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Snow Accumulation ◽  
Climate System ◽  
Shortwave Radiation ◽  
Climate Projections ◽  
Change Models ◽  
Snow Cover Extent

Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy budget. However, with the growing seasonal divergence of SCE over the Northern Hemisphere (NH) in the past two decades, novel insights pertaining to SnRF are lacking. Consequently, the contribution of SnRF to TOA shortwave radiation anomalies still remains unclear. Utilizing the latest datasets of snow cover, surface albedo, and albedo radiative kernels, this study investigated the distribution of SnRF over the NH and explored its changes from 2000 to 2019. The 20-year averaged annual mean SnRF in the NH was −1.13 ± 0.05 W m−2, with a weakening trend of 0.0047 Wm−2 yr−1 (p < 0.01) during 2000–2019, indicating that an extra 0.094 W m−2 of shortwave radiation was absorbed by the Earth climate system. Moreover, changes in SnRF were highly correlated with satellite-observed TOA shortwave flux anomalies (r = 0.79, p < 0.05) during 2000–2019. Additionally, a detailed contribution analysis revealed that the SnRF in snow accumulation months, from March to May, accounted for 58.10% of the annual mean SnRF variability across the NH. These results can assist in providing a better understanding of the role of snow cover in Earth’s climate system in the context of climate change. Although the rapid SCE decline over the NH has a hiatus for the period during 2000–2019, SnRF continues to follow a weakening trend. Therefore, this should be taken into consideration in current climate change models and future climate projections.

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