scholarly journals Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products

2017 ◽  
Vol 9 (8) ◽  
pp. 774 ◽  
Author(s):  
Xiaoduo Pan ◽  
Xin Li ◽  
Guodong Cheng ◽  
Rensheng Chen ◽  
Kuolin Hsu
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Impact Analysis ◽  
Forecast Model ◽  
Mountainous Region ◽  
Fractional Snow Cover ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Weather Research

scholarly journals Satellite monitoring of blue-ice extent near Byrd Glacier, Antarctica

2004 ◽  
Vol 39 ◽  
pp. 223-230 ◽  
Author(s):  
Ian C. Brown ◽  
Ted A. Scambos
Keyword(s):  
Snow Cover ◽  
Satellite Monitoring ◽  
Landsat Images ◽  
Moderate Resolution ◽  
Summer Maximum ◽  
Resolution Imaging ◽  
Snowfall Event ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal And Interannual Variations

AbstractWe use satellite images to track seasonal and interannual variations in blue-ice extent over the past 30 years near Byrd Glacier on the East Antarctic plateau. The study areas have low slope and few nearby nunataks, which may increase their climate sensitivity. A threshold-based algorithm sensitive to snow grain-size is used to analyze 56 Moderate Resolution Imaging Spectroradiometer (MODIS) images over three recent summer seasons. Seasonal blue-ice exposure grows rapidly in late spring, and peaks by late December. Exposure is relatively constant between late December and mid-January, then declines in February. We interpret this cycle as due to removal and re-accumulation of patchy snow. Interannual changes in blue-ice area may be estimated by tracking the near-constant summer maximum extent period. Fifteen mid-summer Landsat images, spanning 1974–2002, were analyzed to determine long-term variations. Interannual area changes are 10–30%; however, the MODIS data revealed that the exposed blue-ice area can be sharply reduced for up to 2 weeks after a snowfall event; and in the 2001/02 season, patchy snow cover persisted for the entire summer. The combination of MODIS seasonal and Landsat interannual data indicates that blue-ice areas can be climate-sensitive. The strong feedback between snow cover and surface energy balance implies that blue-ice areas could rapidly decrease due to climate-related increases in snowfall or reduced ablation.

A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

2017 ◽  
Vol 12 (4) ◽  
pp. 793-805 ◽  
Author(s):  
Tong Liu ◽  
Morimasa Tsuda ◽  
Yoichi Iwami ◽  
◽  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Indus Basin ◽  
Maximum Magnitude ◽  
Upper Indus Basin ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Tarbela Dam

This study considered glacier and snow meltwater by using the degree–day method with ground-based air temperature and fractional glacier/snow cover to simulate discharge at Skardu, Partab Bridge (P. Bridge), and Tarbela Dam in the Upper Indus Basin during the monsoon season, from the middle of June to the end of September. The optimum parameter set was determined and validated in 2010 and 2012. The simulated discharge with glaciermelt and snowmelt could capture the variations of the observed discharge in terms of peak volume and timing, particularly in the early monsoon season. The Moderate Resolution Imaging Spectroradiometer (MODIS) daily and eight-day snow cover products were applied and recommended with proper settings for application. This study also investigated the simulations with snow packs instead of daily snow cover, which was found to approach the maximum magnitude of observed discharge even from the uppermost station, Skardu.This study estimated the glacier and snow meltwater contribution at Skardu, Partab Bridge, and Tarbela as 43.2–65.2%, 22.0–29.3%, and 6.3–19.9% of average daily discharge during the monsoon season, respectively. In addition, this study evaluated the main source of simulation discrepancies and concluded that the methodology proposed in the study worked well with proper precipitation.

scholarly journals The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

2020 ◽  
Vol 12 (22) ◽  
pp. 3693
Author(s):  
Hongyu Zhao ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Guanghui Huang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Absolute Error ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Endmember Extraction ◽  
Analysis Model ◽  
Fractional Snow Cover ◽  
Extraction Algorithm ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

scholarly journals Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

2015 ◽  
Vol 9 (5) ◽  
pp. 4997-5020 ◽  
Author(s):  
C. L. Huang ◽  
H. W. Wang ◽  
J. L. Hou
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western China ◽  
Kriging Interpolation ◽  
Snow Cover Area ◽  
Smoothing Effects ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

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