Spatiotemporal changes of snow metrics in mountainous data-scarce areas using reanalyses

Abstract

Mountain snowmelt is a critical water resource for achieving food and water security in drylands. Climate change impacts the snowmelt and consequently water resources reliability in downstream communities in drylands. Data limitation is one of the main challenges for assessing such impacts. This study assessed the trend in snow depth (SD), snowy day to wet day (SDWD), and snow phenology-related metrics in data-scarce snowy areas of Iran over 1987–2017. Furthermore, the contribution of temperature warming, precipitation and Arctic Oscillation (AO) anomalies to the snow metrics trends was investigated. The analyses were performed using the more accurate reanalyses selected between ERA-Interim and ERA5. The trend magnitude and significance were also assessed during the December-January-February (DJF), March-April (MA), and December-January-February-March-April (DJFMA) using the Sen’s estimator and Mann-Kendall (MK) test, respectively. Given the ERA5′s superiority, this product was employed for the trend and contribution analyses. The SD, SDWD and snow cover duration (Sdur) had a decreasing trend at more than 90% of study area. Except for snow cover onset date (Ds), MK detected a significant trend in the snow metrics at 40% or more of the studied regions. The more humid regions experienced a greater SD reduction. The SDWDDJF also decreased by less than 1% for the areas having average winter wintertime temperature below the threshold of −2.80 °C. The downward trend in Sdur was largely due to the earlier snowmelt rather than later Ds. A decline of 5 > day per decade was found for Sdur at the areas with DJFMA temperature below the melting point. The SD, SDWD and phenology metrics changes can be accounted for by temperature warming in most regions. The SDWD-AO relationship was statistically significant for the majority of the cases. The AO anomalies seem to impact SDWD via affecting wintertime temperature. The decrease of snow metrics indicates the occurrence of more severe and frequent dry snow droughts in the studied sub-basins. Thus, drought adaptation strategies need to consider the patterns of snow metrics in addition to total precipitation under global warming. The ERA5 outputs can be applied for evaluating snow drought under data scarcity.