scholarly journals Review of “Characterizing spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: The clutha Catchment, New Zealand”

2019 ◽  
Author(s):  
Anonymous
2019 ◽  
Vol 23 (8) ◽  
pp. 3189-3217 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

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.


2019 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

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. Metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Raster principal components analysis (rPCA) was applied to maps of annual SCD anomaly to characterise modes of spatial variability whilst preserving temporal signals. Semi-distributed analysis between SCD and temperature and precipitation anomalies allowed sensitivity of SCD to climatic forcings to be assessed spatially. 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 days per year. A reduction in SCA through mid-winter, prior to a second peak in August and persistent throughout the time series 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. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. rPCA and semi-distributed analysis of SCD anomalies reveal 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.


2021 ◽  
Author(s):  
Kathrin Naegeli ◽  
Nils Rietze ◽  
Jörg Franke ◽  
Martin Stengel ◽  
Christoph Neuhaus ◽  
...  

<p>The Hindu Kush Himalaya (HKH), the worlds ‘water tower’, contains the largest volume of snow and ice outside of the polar ice sheets and is the headwater area of Asia’s largest rivers. Due to the complex topography and its great spatial extent the HKH is characterised by variable temperature and precipitation pattern and thus exhibits large heterogeneity in the presence of seasonal snow cover (SSC). Previous studies usually focused on regional studies of snow cover area percentage or the influence of snow melt on the local hydrological system. Here we present a systematic overview of spatio-temporal SSC variability of the entire HKH region on a climate relevant time scale (four decades).</p><p>Our results are based on Advanced Very High Resolution (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km since 1982 up to today. This unique dataset is exceptionally valuable to derive pixel-based SSC information using a Normalised Difference Snow Cover (NDSI) approach including additional thresholds related to topography and land cover, and developed in the frame of ESA CCI+ snow.  Calibrated and geocoded reflectance data and a consistent cloud mask, derived in the ESA CCI cloud project, are used. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.</p><p>The dataset allows analysis of the state and trends of SSC at regional and sub-regional level. We thus investigated spatio-temporal evolution and long-term variability of SSC for the entire HKH as well as for 14 hydrological basins. We find large spatial difference in the amount of SSC depending on the regional elevation and precipitation characteristics. Furthermore, we investigate SSC phenology, which is directly linked to climate change and thus of high relevance for seasonal water storage and mountain streamflow. Our analysis indicates a significant decline in snow cover area percentage (SCA %) during warm and dry summer month and a decreasing tendency from high winter through spring to early summer. At the hydrological basin level, no significant long-term trend was detected, however, both western and central basins indicate a decrease in SCA % and generally the latest years are strongly negative. Moreover, we examine SCA % anomalies at the highest available temporal frequency (daily information) and reveal an overall shortening of the SSC occurrence and a general decrease of SSC extent in the HKH region.</p>


2013 ◽  
Vol 37 (4) ◽  
pp. 296-305 ◽  
Author(s):  
Qi-Qian WU ◽  
Fu-Zhong WU ◽  
Wan-Qin YANG ◽  
Zhen-Feng XU ◽  
Wei HE ◽  
...  

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