An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year(HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer(MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin(NJ), Downstream Songhua River Basin(SD) and Upstream Songhua River Basin(SU). Snow cover duration(SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors(elevation, aspect and slope), two climatic factors(precipitation and air temperature) and one vegetation index(Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree(CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.

Impacts of snow cover duration on vegetation spring phenology over the Tibetan Plateau

Aims Snow cover occupies large percentage of land surface in Tibetan Plateau.Snow cover duration(SCD)during non-growing seasons plays a critical role in regulating alpine vegetation’s phenology by affecting the energy budgets of land surface and soil moisture con-ditions.Different period’s snow cover during non-growing season may have distinct effect on the vegetation’s phenology.Start of season(SOS)has been observed advanced under the ongoing cli-mate change in the plateau,but it still remains unclear how the SCD alters the SOS.This study attempts to answer the following questions:(i)What is the pattern of spatial and temporal variations for SCD and grassland SOS?(ii)Which period’s SCD plays a critical role in grassland’s SOS?Methods The remote sensing datasets from the Moderate Resolution Imaging Spectroradiometer(MODIS)were utilized to compute the SOS and SCD on the Tibetan Plateau over 2003-2015.The Asymmetric Gaussian function was applied to extract SOS.We also explored the spatial pattern and temporal variation of SOS and SCD.Then,by using linear correlation coefficients,we investigated the driving effects of different period’s non-growing season SCD on SOS.Important Findings The non-growing season SCD slightly decreased during 2003-2015,while SOS exhibited an overall advancing trend.Advanced trends in SOS were observed in the eastern plateau,and the delayed trends were mainly founded in western plateau.Snow cover area exhibited two separate peaks during autumn and late winter over the plateau.Extended SCD regions mainly distributed in middle-east of the plateau,while shrunken SCD distributed in other regions of the plateau.SCD of different seasons caused distinct effects on vegetation SOS.Lengthened autumn SCD advanced SOS over the eastern plateau.The slightly lengthened SCD postponed SOS over the western plateau.In the wet meadow regions,advanced SOS was positively associated with SCD during the entire non-growing season,whereas for the dry steppe,SCD over the preseason played a more dominant role.The SCD of previous autumn and winter also showed lag effect on SOS over meadow regions to a certain extent.This study confirmed the importance of SCD to phenological pro-cesses at the beginning of growing season and further suggested that role of SCD should be discriminated for different periods and for dif-ferent heat-water conditions.With the lag effects and SCD’s distinct effect of different seasons considered,predictions on the Tibetan Plateau’s spring phenology could be improved.

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes.The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover.We used 500 m Moderate Resolution Imaging Spectroradiometer(MODIS)datasets during 2001–2010 to examine the snow–vegetation relationships,specifically,(1)the influence of snow melting date on vegetation green-up date and(2)the effects of snow cover duration on vegetation greenness.The results showed that the alpine vegetation responded strongly to snow phenology(i.e.,snow melting date and snow cover duration)over large areas of the Qinghai-Tibetan Plateau.Snow melting date and vegetation green-up date were significantly correlated(p<0.1)in 39.9% of meadow areas(accounting for 26.2% of vegetated areas)and 36.7% of steppe areas(28.1% of vegetated areas).Vegetation growth was influenced by different seasonal snow cover durations(SCDs)in different regions.Generally,the December–February and March–May SCDs played a significantly role in vegetation growth,both positively and negatively,depending on different water source regions.Snow’s positive impact on vegetation was larger than the negative impact.