Snow occurs in the High Drakensberg of southern Africa approximately eight times per annum. Snow cover is frequently captured by Landsat satellite imagery, which provide data for the monitoring of snow cover in other regions of the world. Together with a digital elevation model, repetitive snow cover data are used to analyse the distribution of snow cover in the High Drakensberg study area. The effect that the regional and local topography, latitude, and climatic conditions have on the spatial distribution of snow and the function that temperature, wind, altitude, aspect and slope gradient play in the preservation of snow cover are examined. The results of the spatial study allow for the identification of sites that support the accumulation of snow. Specific active and relict geomorphological features were surveyed and correlated spatially to the contemporary snow cover. Among such features are linear debris ridges on south-facing valley slopes in the High Drakensberg. These appeared similar to glacial features found elsewhere in the world and are thus significant in a long-standing and highly conjectured debate over the validity of possible plateau, cirque and niche glaciation in the region. Late-lying snow cover favours gently sloping south- and southeast-facing aspects at altitudes from 3000 m ASL to just below the highest peaks in the region near 3450 m ASL, above which higher insolation levels on the flat mountain summits provides unfavourable conditions. Snow cover immediately adjacent to the Drakensberg escarpment ablates quickly whilst snow cover at high altitudes in the Lesotho interior experiences better preservation conditions. Latitude has no obvious impact on the distribution of snow cover due to the dominant role of topography in the High Drakensberg other than a limiting of snowfall to regions south of 29°S in late spring. Various synoptic conditions produce snowfall in the region, with cold fronts associated with midlatitude cyclones producing the majority of snow cover. A strong correlation exists between the spatial distribution of snow cover and specific geomorphological features. Observed linear debris ridges are located on slopes that experience frequent contemporary snow cover, lending credence for a glacial origin of the ridges during a period of colder environmental conditions.

This thesis deals with the possibility of using webcams as a source of spatial data for snow occurrence. The aim of this study is to propose a suitable method of snow cover detection from web camera images. From a sample of 6 webcams of the Czech Hydrometeorological Institute (CHMI) the snow cover is detected by pixel classification methods. The effect of training file size on the accuracy of classification is examined and the overall accuracy achieved by the SVM method was shown to be 97.46%. This study also aims to propose a system for determining the proportion of snow-covered areas. The algorithm consists of several sub-steps: filtering and registration of images, detection of snow, introduction of a coordinate system, calculation of the size of the surveyed area and the proportion of snow-covered area. The designed model can be used for automatic processing of images for various webcams. The melting curves of the snow cover are generated from the obtained daily values of the snow covered area. The results are validated using data from selected CHMI stations. The proposed and parameterized model confirms the possibility of successful use of webcams as a complement to ground measurement of meteorological stations and for the validation of remote sensing products.

The projected increase of shrubs across the Arctic is expected to alter patterns of snow cover, which may affect the phenology and survival of arthropods such as spiders. In this study, we simulated shrub encroachment on a series of tundra plots and examined the effects on the spider assemblages during the following growing season. Our simulated shrub treatment did not affect the abundance or composition of spider communities over the season; however, adults from the dominant genus Pardosa (Lycosidae) had significantly higher body mass on treatment plots. This difference in mass was observed following snow melt and persisted until halfway through the growing season. Given the importance of spiders as arthropod predators and as food sources for breeding birds, such a change in summer body mass could represent a shift in spiders’ functional contributions to Arctic ecosystems.

The projected increase of shrubs across the Arctic is expected to alter patterns of snow cover, which may affect the phenology and survival of arthropods such as spiders. In this study, we simulated shrub encroachment on a series of tundra plots and examined the effects on the spider assemblages during the following growing season. Our simulated shrub treatment did not affect the abundance or composition of spider communities over the season; however, adults from the dominant genus Pardosa (Lycosidae) had significantly higher body mass on treatment plots. This difference in mass was observed following snow melt and persisted until halfway through the growing season. Given the importance of spiders as arthropod predators and as food sources for breeding birds, such a change in summer body mass could represent a shift in spiders’ functional contributions to Arctic ecosystems.

Résumé : Cette recherche présente l'analyse spatio-temporelle de l'évolution conjointe de l'augmentation de la présence arbustive et de la dynamique de la fonte de la neige au Nunavik, Québec, Canada. Cette zone est caractérisée par la complexité de l'interaction de multiples changements simultanés de la température, de la couverture de la neige ainsi que de la pousse végétative. La première partie de ce travail consiste à faire l'analyse de l’évolution temporelle de ces multiples changements. Cette analyse a nécessité la mise en place d’une importante base de données climatiques, satellitaires et de couverture de sol à plusieurs échelles, sur une période allant jusqu'à 60 ans, soit de 1950-2012. La deuxième partie du travail consiste à faire l'analyse spatiale à haute résolution de l’influence de la fraction du couvert arbustif sur la fonte de la neige. L'analyse et l'interprétation des résultats obtenus dans la première partie montrent clairement un changement climatique significatif sur la région étudiée, découpée en 3 bandes de latitude correspondant à la toundra, la taïga ouverte et à la taïga forestière, respectivement du Nord au Sud. Ce changement de climat correspond à un réchauffement marqué, entre 0.75°C et 1.57°C par décade entre les zones 1 (toundra) et 3 (taïga forestière) respectivement. On peut noter que la hauteur de neige maximale annuelle a diminué dans les trois zones alors que les précipitations hivernales ont augmenté en zone 1 et 3 sur les 45 dernières années. Les résultats montrent une nette augmentation de la végétation arbustive dans les zones 2 et 3 (LAI plus élevé de 100% dans la zone 3 par rapport à la zone 1). L'impact de la végétation a été analysé à partir de la durée de fonte relative entre le début de la fonte et la disparition de la neige. Il apparait clairement que la végétation active la fonte précocement, allongeant ainsi significativement la durée de fonte (+600%). Cependant, l'impact de la végétation ne retarde pas la date de fin du couvert nival qui est de plus en plus précoce pour les zones 2 et 3. L'analyse spatiale à haute résolution montre que la présence arbustive entraine une date de fin de neige plus précoce par rapport au sol nu. Cette étude démontre clairement que la croissance de la végétation qui résulte du réchauffement climatique impacte la dynamique du couvert nival, aussi affectée par ce réchauffement. Une étude approfondie des processus en causes avec des mesures in situ appuyées par leur modélisation permettrait de mieux comprendre ces phénomènes. // Abstract : This study presents a spatial-temporal analysis of the joint evolution of the increase of shrubiness and the dynamics of snowmelt in Nunavik, Quebec, Canada. This zone is characterized by the complexity of the interaction of multiple changes of temperature, snow cover and vegetation growth. The first part of this study analyzes the temporal evolution of these changes. The analysis required the use of a large database on climate, satellite data and ground cover at multiple scales over a period of up to 60 years, from 1950 to 2012. The second part of the study consists of a spatial high-resolution analysis of the influence of the fraction of shrub cover on snowmelt. The analysis and interpretation of the results clearly show a significant climate change over the study area, divided into three latitudinal transects corresponding to tundra, open taiga and forested taiga. A significant warming of 0.75 ° C and 1.57 ° C per decade was experienced between zones 1 (tundra) and 3 (forested taiga) respectively. The maximum annual snow depth on the ground decreased over the 3 zones studied while winter precipitations increased in zones 1 and 3 over the last 45 years. The results show a significant increase in shrub vegetation in zones 2 and 3. The impact of the vegetation on snow was analyzed with melt duration (from melt onset to complete melt). It appears clearly that the vegetation triggers the melting process earlier and significantly extends the melt duration (+600%). However, the impact of vegetation does not delay the date of the snow cover disappearance. The high-resolution spatial analysis showed that shrubs cause an earlier snow cover disappearance date than bare soil. This study clearly demonstrates that vegetation growth resulting from global warming impacts the snow cover dynamics, which are also affected by global warming. A thorough study of the processes with in-situ measurements supported by models would help gaining a better comprehension of these phenomena.

As the need to understand climate induced changes increases, so too does the need to understand the long-term spatial-temporal characteristics of snow cover and snow water equivalence (SWE). Snow cover and SWE are useful indicators of climate change. In this research, we combine methods from spatial statistics, geographic information systems (GIS), time-series analysis, ecosystems classification, cluster analysis, and remote sensing, to provide a unique perspective on the spatial-temporal interactions of SWE. We show that within the Canadian Prairies, extreme SWE are becoming more spatially constrained, and may cause some regions to be more prone to flooding. As well, we find that the temporal characteristics of SWE are not captured by current ecological management units, highlighting the need for Canadian ecological management units that consider winter conditions. We then address this need by developing methods designed to generate geographically distinct SWE regimes. These regimes are used to partition the landscape into winter-based management units, and compared with conventional summer based units. We find that regional variations in the ability of current ecological units to capture SWE characteristics exist, and suggest that SWE regimes generated as a result of this analysis should be used as guidelines for developing winter-based management units in conjunction with current ecological stratifications.

Accurate knowledge of water content in seasonal snow can be helpful for water resource management. In this study, a distributed temperature index snow model based on temperature and precipitation as forcing data, is used to estimate snow storage in the Jollie catchment approximately 20km east of the main divide of the central Southern Alps, New Zealand. The main objective is to apply a frequently used assimilation method, the ensemble Kalman square root filter, to assimilate remotely sensed snow covered area into the model and evaluate the impacts of this approach on simulations of snow water equivalent. A 250m resolution remotely sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS), specifically tuned to the study location was used. Temperature and precipitation were given on a 0.055 latitude/longitude grid. Precipitation was perturbed as input into the model, generating 100 ensemble members, which represented model error. Only observations of snow covered area that had less that 25% cloud cover classification were used in the assimilation precess. The error in the snow covered area observations was assumed to be 0.1 and grow linearly with cloud cover fraction up to 1 for a totally cloud covered pixel. As the model was not calibrated, two withholding experiments were conducted, in which observations withheld from the assimilation process were compared to the results. Two model states were updated in the assimilation, the total snow accumulation state variable and the total snow melt state variable. The results of this study indicate that the model underestimates snow storage at the end of winter and/or does not detect snow fall events during the ablation period. The assimilation method only affected simulated snow covered area and snow storage during the ablation period. That corresponded to higher correlation between modelled snow cover area and the updated state variables. Withholding experiments show good agreement between observations and simulated snow covered area. This study successfully applied the ensemble Kalman square root filter and showed its applicability for New Zealand conditions.

abstract: Sea ice algae dominated by diatoms inhabit the brine channels of the Arctic sea ice and serve as the base of the Arctic marine food web in the spring. I studied sea ice diatoms in the bottom 10 cm of first year land-fast sea ice off the coast of Barrow, AK, in spring of 2011, 2012, and 2013. I investigated the variability in the biomass and the community composition of these sea-ice diatoms between bloom phases, as a function of overlying snow depth and over time. The dominant genera were the pennate diatoms Nitzschia, Navicula, Thalassiothrix, and Fragilariopsis with only a minor contribution by centric diatoms. While diatom biomass as estimated by organic carbon changed significantly between early, peak, and declining bloom phases (average of 1.6 mg C L-1, 5.7 mg C L-1, and 1.0 mg C L-1, respectively), the relative ratio of the dominant diatom groups did not change. However, after export, when the diatoms melt out of the ice into the underlying water, diatom biomass dropped by ~73% and the diatom community shifted to one dominated by centric diatoms. I also found that diatom biomass was ~77% lower under high snow cover (>20 cm) compared to low snow cover (<8 cm); however, the ratio of the diatom categories relative to particulate organic carbon (POC) was again unchanged. The diatom biomass was significantly different between the three sampling years (average of 2.4 mg C L-1 in 2011, 1.1 mg C L-1 in 2012, and 5.4 mg C L-1 in 2013, respectively) as was the contribution of all of the dominant genera to POC. I hypothesize the latter to be due to differences in the history of ice sheet formation each year. The temporal variability of these algal communities will influence their availability for pelagic or benthic consumers. Furthermore, in an Arctic that is changing rapidly with earlier sea ice and snowmelt, this time series study will constitute an important baseline for further studies on how the changing Arctic influences the algal community immured in sea ice. / Dissertation/Thesis / Masters Thesis Biology 2014

Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah-MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two-digital hydrological unit code regions of the continental United States (CONUS). In general, Noah-MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah-MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS-2) LSMs, Noah-MP shows a better ability to simulate runoff and a comparable skill in simulating R-n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

Increased temperatures, changed snow conditions, increasing demand for skiing and other winter sports lead to increased demand and production of artificial snow. Besides the positive aspects of artificial snow, it is important to understand potentially negative effects of artificial snow on the environment and vegetation. This study investigated the differences between artificial snow and natural snow in two adjacent ski slopes. This was done by measuring and comparing the snow depth, duration of the snow cover, snow density, and ground vegetation between the slopes. Snow depth and density showed differences between artificial snow and natural snow. The artificial snow cover stayed about 11 days longer than the natural snow. A few variations in the vegetation were also found; earlier flowering and more species and colors in the natural snow slope compared to in the slope using artificial snow. Therefore, it is clear that there is a difference between artificial snow and natural snow, and that this results in effects on the environment, but it is difficult to assess to what extent and how large the negative impact is. For more general conclusions, I think it is important to study the vegetation under artificial snow during a longer period and in several areas.