Characterising spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: the Clutha Catchment, New Zealand

Abstract

Abstract. A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

“…Comparisons of MODIS-derived snowlines from high-resolution DEMs and field-based studies have shown good agreement [5,38]. Additionally, the RSLE method by [14] has been widely used to investigate snow dynamics [37,[39][40][41][42]. This method estimates the elevation at which the number of snow-free pixels above the elevation line and the number of snowy pixels below the elevation line are at their minimum above and below the elevation line.…”

Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019)

“…google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1, last accessed on Remote Sens. 2021, 13, 2945 5 of 26 26 July 2021 and https://developers.google.com/earth-engine/datasets/catalog/MODIS_ 006_MYD10A1, last accessed on 26 July 2021). MOD10A1 scenes are processed from the data acquired by MODIS onboard Terra platform, with a morning overpass (approximately 10:30 a.m. at the equator), while MYD10A1 is generated from the radiance acquired by MODIS onboard the AQUA platform, cycling the Earth on an afternoon overpass (approximately 1:30 p.m. at the equator).…”

“…Compared to AVHRR, MODIS benefits from a finer spatial resolution (500 m) and the availability of validated snow cover products such as MOD10A1 [25], and although it has a shorter period of data coverage (since 2000), it has now reached 20 years in orbit, building an invaluable data record and approaching the minimum time required for climatological analysis. Several studies have employed MODIS, based on either the daily or the 8-day product, to investigate the spatial distribution and interannual variability in snow cover at the regional scale and to estimate the runoff characteristics [16,26,27]. Some studies have also included the Alps as part of a larger area: for instance, Dietz et al [28] observed snow cover characteristics in Europe from 2000 to 2011, while Notarnicola et al [29] investigated snow cover changes over major mountain chains of the planet from 2000 to 2018.…”

Elevation‐dependent response of snow phenology to climate change from a remote sensing perspective: A case survey in the central Tianshan mountains from 2000 to 2019

“…It is noted that due to the scarcity of observation stations in most mountainous areas over the world, it is difficult to adequately quantify the spatiotemporal variability of snow cover in the mountainous regions purely by station data (Woo and Thorne, 2006; Pu et al, 2007). The snow products of the Moderate Resolution Imaging Spectroradiometer (MODIS) have been widely used to describe the temporal and spatial distribution of snow in mountain areas (Hall and Riggs, 2007; Liang et al, 2008; Choubin et al, 2019), showing that the altitude is an indispensable and important factor influencing the temporal and spatial distribution of snow phenology (Redpath et al, 2019). For example, Li et al (2018) analysed the temporal and spatial distribution and the trend characteristics of the snow cover fraction (SCF) in seven upstream river basins on the Qinghai‐Tibet Plateau, finding that the distribution of snow cover is highly dependent on elevations, with a higher SCF and a later onset of snow melt at the higher elevation zones than at the lowers.…”