1Center of Integrated Geographic Information Analysis, and Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat - sen University, Guangzhou, China, 510275
2State Key Laboratory of Des ert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China, 830011
3Heilongjiang Climate Data Center, Ha’erbin, China, 150001
4Department of Geography, University of Cincinnati, Cincinnati, OH, USA
1Center of Integrated Geographic Information Analysis, and Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat - sen University, Guangzhou, China, 510275
2State Key Laboratory of Des ert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China, 830011
3Heilongjiang Climate Data Center, Ha’erbin, China, 150001
4Department of Geography, University of Cincinnati, Cincinnati, OH, USA
Received: 24 Mar 2016 – Accepted for review: 30 Apr 2016 – Discussion started: 12 May 2016
Abstract. The study applies the improved cloud-free Moderate Resolution Imaging Spectral radiometer (MODIS) daily snow cover product (MODMYD_MC) to analyze the snow cover variations from snow Hydrologic Year (HY) HY2000 to HY2013 in the Amur River Basin (ARB), northeast Asia. The fractions of forest cover were 38 %, 63 % and 47 % in 2009 in China (the southern ARB), Russia (the northern ARB) and ARB, respectively. Forest demonstrates complex influences on the snow accumulation and melting processes and on optical satellite snow cover mapping. Validation results show that MODMYD_MC has a snow agreement of 88 % against in situ snow depth (SD) observations (SD≥4cm). The agreement is about 10 % lower at the forested stations than at the non-forested stations. Snow Cover Durations (SCD) from MODMYD_MC are 20 days shorter than ground observations (SD ≥ 1 cm) at the forested stations, while they are just 8 days shorter than ground observations (SD ≥ 1 cm) at the non-forested stations. Annual mean SCDs in the forested areas are 21 days shorter than those in the nearby farmland in the SanJiang Plain. SCD and Snow Cover Fraction (SCF) are negatively correlated with air temperature in ARB, especially in the snow melting season, when the mean air temperature in March and April can explain 86 % and 74 % of the mean SCF variations in China and Russia, respectively. From 1961 to 2015, the annual mean air temperature presented an increase trend by 0.33 ℃/decade in both China and Russia, while it had a decrease trend during the study period from HY2000 to HY2013. The decrease of air temperature resulted in an increase of snow cover although the increase of snow cover was not significant above the 90 % confidence level. SCD and SCF had larger increase rates in China than in Russia, and they were larger in the forested area than in the nearby farmland in similarly climatic settings in the SanJiang Plain. Air temperature began to increase again in 2014 and 2015, and the increasing air temperature in ARB projects a decrease of snow cover extent and periods in the coming years.
The study investigates the snow cover variations in the Amur River Basin (ARB) using MODIS observations from 2000–2014. Forest (about 50 %) demonstrates complex influences on the snow accumulation and melting processes and on optical satellite snow cover mapping. The snow cover increase in ARB is associated with the decrease of air temperature during the study period. The increasing air temperature in ARB projects a decrease of snow cover extent and periods in the coming years.
The study investigates the snow cover variations in the Amur River Basin (ARB) using MODIS...
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