Snow cover trend analysis using Interactive Multisensor Snow and Ice Mapping System data over Turkey

Abstract

ABSTRACT:The daily snow cover data from Interactive Multisensor Snow and Ice Mapping System (IMS) by National Oceanic and Atmospheric Administration's National Environmental Satellite Data and Information Service (NOAA/NESDIS) covering the 2004-2012 time period is used in this study. In the first part of the study, daily IMS product is validated over Turkey using daily ground truth snow depth measurements from 219 climatological and synoptic sites. The categorical statistics of probability of detection (POD) and false alarm rates (FAR) are provided both for whole Turkey and with respect to geographical sub-regions. The overall POD and FAR amounts for Turkey are obtained as 73.55 and 25.11%, respectively. The performed analysis indicates an increase in IMS accuracies as the snowpack depth at the stations increase. In the second part of the study, the nonparametric Mann-Kendall test is performed to analyse the snow cover trend over Turkey with respect to sub-regions considering the whole year and each season separately. A negative trend in snow-covered area percentage is obtained for Turkey at the significance level of α = 0.05. From the seasonal perspective for Turkey, negative trend for spring and summer seasons, positive trend for autumn season and combination of negative and no trend for winter season are obtained at the same significance level. Almost the same results are valid for the sub-regions with some local and seasonal variations.

Spatial and Temporal Variations of Snow Cover in the Karoon River Basin, Iran, 2003–2015

“…Investigations at similar latitudes with longer records revealed trends in the annual maximum snow line [36] and in the elevation at which snowfall transitions to rainfall [37], and both variables impact SCD. In the Pir-Panjal range of the Himalayas (~32.33 • N, 77.50 • E), Verdhen et al [38] suggested that the yearly maximum snow line altitude increased by 11 m per year from 1983 to 2009.…”

“…The findings revealed that the Indus Basin had a unique increasing trend in snow cover, whereas the Ganga and Brahmaputra Basins had no significant trend over this period. In another investigation, Sönmez et al [36] examined the trend of snow cover over Turkey from 2002 to 2012 using Interactive Multisensor Snow and Ice Mapping System (IMS) data and, at the significance level of 0.95, they detected a negative trend for spring and summer, a positive trend for autumn, and a combination of negative and no trend for winter. Using the MODIS snow data from 2000 to 2009, Khadka et al [37] revealed that over the Tamakoshi Basin in the Hindu Kush Himalayan region there was a negative trend in winter and spring snow cover, while autumn snow cover showed a positive trend for this period, but the results were not found to be significant at the 95% confidence level.…”

Spatio-temporal trends in the hydroclimate of Turkey for the last decades based on two reanalysis datasets

“…8): both indicate no significant trends despite the differences in terms of SWE magnitudes. Although Sönmez et al (2014) indicated dominantly negative trends for the snow cover over Turkey, the study period was relatively short (2004-2012) for a reliable trend analysis. Although the validation of the runoff trends could not be carried out with in situ data, a correlation analysis showed that runoff and precipitation are positively correlated extensively for both reanalysis datasets (Fig.…”

“…Yucel et al (2014) investigated the regional trends in hydroclimatic variables in eastern Anatolia, Turkey, between 1970 and 2010 based on the streamflow and meteorological point measurements. The seasonal snowmelt runoff in the mountainous basins of eastern Turkey has experienced changes over the past several decades Sönmez et al, 2014). Sen et al (2011) indicate that streamflow timings of the Euphrates and Tigris rivers shifted to the earlier days between two consecutive 17-year periods (i.e., 1972-1988 and 1990-2006).…”

Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey

“…This large areal variability and the effect of snow cover (SC) on the energy fluxes make monitoring of SC an important issue for climate, meteorology, hydrology, and even for transportation activities. However, inadequacy of in situ measurement networks for snow records is mentioned in Mognard (2003), Rees (2006), and Sönmez et al (2014). Moreover, SC is a dynamic medium, where major changes during spring period may happen in a few days time.…”