Snow is a common overlying surface during winter-time and the redistribution of snow by wind is a very important concept for any hydrological research project located within the cryosphere. Wind redistributes snow by eroding it from areas of high wind speed, such as ridge tops and windward slopes, and deposits it in areas of lower wind speeds, such as the lees of ridge tops, vegetation stands, and topographic depressions. The accurate modelling of blowing snow processes such as erosion, deposition, and sublimation have proven to be rather problematic. The largest issue that many modellers must deal with is the accurate collection of solid precipitation throughout the winter season. Without this, incorrect energy and mass balances can occur. This thesis makes use of a new method of acquiring solid precipitation values through the use of an SR50a ultrasonic snow depth sensor and then incorporates it into a version of the Cold Regions Hydrological Model (CRHM) which includes the Prairie Blowing Snow Model (PBSM) and the Minimal Snowmelt Model (MSM) modules. The model is used to simulate seasonal snow depth over an agricultural field in southern Ontario, Canada and is driven with half-hourly locally acquired meteorological data for 83 days during the 2008-2009 winter season. Semi-automated snow surveys are conducted throughout the winter season and the collected in situ snow depth values are compared to the simulated snow depth values at multiple scales. Two modelling approaches are taken to temporally and spatially test model performance. A lumped approach tests the model‟s ability to simulate snow depth from a small point scale and from a larger field scale. A distributed approach separates the entire field site into three hydrological response units (HRUs) and tests the model‟s ability to spatially discretize at the field scale. HRUs are differentiated by varying vegetation heights throughout the field site. Temporal analysis compares the simulated results to each day of snow survey and for the entire field season. Model performance is statistically analyzed through the use of a Root Mean Square Difference (RMSD), Nash-Sutcliffe coefficient (NS), and Model Bias (MB). Both the lumped and distributed modelling approaches fail to simulate the early on-set of snow but once the snow-holding capacities are reached within the field site the model does well to simulate the average snow depth during the latter few days of snow survey as well as throughout the entire field season. Several model limitations are present which prevent the model from incorporating the scaling effects of topography, vegetation, and man-made objects as well as the effects from certain energy fluxes. These limitations are discussed further.

Snow is a common overlying surface during winter-time and the redistribution of snow by wind is a very important concept for any hydrological research project located within the cryosphere. Wind redistributes snow by eroding it from areas of high wind speed, such as ridge tops and windward slopes, and deposits it in areas of lower wind speeds, such as the lees of ridge tops, vegetation stands, and topographic depressions. The accurate modelling of blowing snow processes such as erosion, deposition, and sublimation have proven to be rather problematic. The largest issue that many modellers must deal with is the accurate collection of solid precipitation throughout the winter season. Without this, incorrect energy and mass balances can occur. This thesis makes use of a new method of acquiring solid precipitation values through the use of an SR50a ultrasonic snow depth sensor and then incorporates it into a version of the Cold Regions Hydrological Model (CRHM) which includes the Prairie Blowing Snow Model (PBSM) and the Minimal Snowmelt Model (MSM) modules. The model is used to simulate seasonal snow depth over an agricultural field in southern Ontario, Canada and is driven with half-hourly locally acquired meteorological data for 83 days during the 2008-2009 winter season. Semi-automated snow surveys are conducted throughout the winter season and the collected in situ snow depth values are compared to the simulated snow depth values at multiple scales. Two modelling approaches are taken to temporally and spatially test model performance. A lumped approach tests the model‟s ability to simulate snow depth from a small point scale and from a larger field scale. A distributed approach separates the entire field site into three hydrological response units (HRUs) and tests the model‟s ability to spatially discretize at the field scale. HRUs are differentiated by varying vegetation heights throughout the field site. Temporal analysis compares the simulated results to each day of snow survey and for the entire field season. Model performance is statistically analyzed through the use of a Root Mean Square Difference (RMSD), Nash-Sutcliffe coefficient (NS), and Model Bias (MB). Both the lumped and distributed modelling approaches fail to simulate the early on-set of snow but once the snow-holding capacities are reached within the field site the model does well to simulate the average snow depth during the latter few days of snow survey as well as throughout the entire field season. Several model limitations are present which prevent the model from incorporating the scaling effects of topography, vegetation, and man-made objects as well as the effects from certain energy fluxes. These limitations are discussed further.

In this thesis freeze-thaw along roads in northern Sweden is examined. The examinations are put in a context of changing climate and its amplification towards the Arctic region on earth. The research focuses on the impact of a warmer climate on ground freeze-thaw and in exten- sion road maintenance in the region. The investigation is presented through two scientific papers, where the first examines how ground temperatures are developed during a single frost season experiment, where a natural accumulation of snow cover and a continual removal of snow cover occur respectively. In the second paper, ground temperature data from sub-Arctic Sweden that has been logged by the Swedish Transport Administration, has been collected and freeze-thaw cycles have been calculated and analysed. The results are related to regional landscape factors and are in the context of regional climate change discussed to reach understanding of challenges for road maintenance in the region and opportunities to reach resilience. The results in Paper 1 show that also a thin cover of snow has impact on the freeze-thaw frequency, duration and intensity that occur in and on the surface of the ground. Furthermore the results show that the ground temperatures rise in due to an increase in snow cover amounts and that this process occurs in several steps. Paper 2 shows that the occurrence of ground freeze-thaw is affected by the proximity to open waters. Warmer temperatures in the air may cause later ice freeze-up and earlier ice break-up on lakes, rivers and on the Gulf of Bothnia and roads in northern Sweden are in general situated on the coast or near rivers. Ground temperatures around 0 °C has a high negative impact on road stability and a warmer and wetter climate in northern Sweden may thus increase road deterioration. The economic development in Sweden stays dependent on extraction of natural resources in sub-Arctic Sweden and thus it is of major concern to main- tain and improve road infrastructure in the region.

No description available.

The objective of this research is to develop a system capable of simulating snow depth and snow water equivalent in the Sherbrooke to Mount-Megantic area of Quebec's Eastern Townships using meteorological and digital terrain data as input.The working hypothesis is that meteorological data may drive a point energy and mass balance snow cover model.The model used was developed by the Hydrologic Research Lab (National Weather Service) which was calibrated for local conditions using field data collected during two winters at several sites on Mount-Megantic. Snow water equivalent and depth are used for calibration and validation of the model. Automated snow sensors were also used to obtain temperature calibration data.The snow surveys and correction of the air temperature for elevation improves the estimates of snow depth and water equivalent.The results suggest that data from the Sherbrooke meteorological stations can be used to estimate the snow cover over the area of Eastern Townships. Air temperature extrapolation across the field area is a challenge. However the simulated snow cover conforms generally well with data observed at several stations throughout the region.

The objective of this research is to explore the information content of repeat-pass cross-track Interferometric SAR (InSAR) with regard to snow, in particular Snow Water Equivalent (SWE) and snow depth. The study is an outgrowth of earlier snow cover modeling and radar interferometry experiments at Schefferville, Quebec, Canada and elsewhere which has shown that for reasons of loss of coherence repeat-pass InSAR is not useful for the purpose of snow cover mapping, even when used in differential InSAR mode. Repeat-pass cross-track InSAR would overcome this problem. As at radar wavelengths dry snow is transparent, the main reflection is at the snow/ground interface. The high refractive index of ice creates a phase delay which is linearly related to the water equivalent of the snow pack. When wet, the snow surface is the main reflector, and this enables measurement of snow depth. Algorithms are elaborated accordingly. Field experiments were conducted at two sites and employ two different types of digital elevation models (DEM) produced by means of cross track InSAR. One was from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), flown in February 2000. It was compared to the photogrammetrically produced Canadian Digital Elevation Model (CDEM) to examine snow-related effects at a site near Schefferville, where snow conditions are well known from half a century of snow and permafrost research. The second type of DEM was produced by means of airborne cross track InSAR (TOPSAR). Several missions were flown for this purpose in both summer and winter conditions during NASA's Cold Land Processes Experiment (CLPX) in Colorado, USA. Differences between these DEM's were compared to snow conditions that were well documented during the CLPX field campaigns. The results are not straightforward. As a result of automated correction routines employed in both SRTM and AIRSAR DEM extraction, the snow cover signal is contaminated. Fitting InSAR DEM's to known topography distorts the snow information, just as the snow cover distorts the topographic information. The analysis is therefore mostly qualitative, focusing on particular terrain situations. At Schefferville, where the SRTM was adjusted to known lake levels, the expected dry-snow signal is seen near such lakes. Mine pits and waste dumps not included in the CDEM are depicted and there is also a strong signal related to the spatial variations in SWE produced by wind redistribution of snow near lakes and on the alpine tundra. In Colorado, cross-sections across ploughed roads support the hypothesis that in dry snow the SWE is measurable by differential InSAR. They also support the hypothesis that snow depth may be measured when the snow cover is wet. Difference maps were also extracted for a 1 km2 Intensive Study Area (ISA) for which intensive ground truth was available. Initial comparison between estimated and observed snow properties yielded low correlations which improved after stratification of the data set.In conclusion, the study shows that snow-related signals are measurable. For operational applications satellite-borne cross-track InSAR would be necessary. The processing needs to be snow-specific with appropriate filtering routines to account for influences by terrain factors other than snow.

This study is a result from a major investigation about the snow conditions in Sweden since the beginning of the twentieth century. For this purpose, data were analysed with respect to the maximum snow depth and the number of days with snow cover every year from some more than forty selected stations. These stations were then divided into different regions and means were calculated for each series. The data are presented in the shape of different histograms in the four following categories; the whole period in request (1900-2003), the latest 43 years (1961-2003), consecutive mean values for every decade and time series with the highest frequented fluctuations equalized. To be able to detect any trends in the plotted time series two statistical methods, simple linear regression and Mann-Kendall’s test, were applied. The calculations belonging to these tests are showed in tables. To be able to answer the question if the global warming can be related to the latest 3-4 decades predominantly warm winters in the southern part of Sweden I have been studying correlations in snow data with respect to the northern hemispheres mean temperature for the winter season. Corresponding estimates of the correlation coefficients have also been made with respect to the Swedish winter mean temperature. The response of the tests shows that it has not been such dramatic change in the snow conditions in the long run. The magnitude of the slope for the adjusted regression lines implies that the maximum snow depth and the number of days with snow cover in average have been on a fairly constant level during the latest hundred years. When it comes to the maximum snow depth one can distinguish a tendency for a small rise in Götaland and northern Norrland. This is also the only cases which are statistical significant for the period in request (1905-2003). For the shorter period 1961-2003 however, the number of days with snow cover has decreased quite substantially in the southern part of Sweden corresponding to a decrease about 40% in Götaland and 20% in Svealand. The test based on simple linear regression gives significant results in both cases while Mann-Kendall only establishes the trend for Götaland. A closer view of the maximum snow depth for the shorter period (1961-2003) does not give the same response but there is at least evidence for a significant decrease in Svealand in the test with simple linear regression. It corresponds to a decrease of about 30% since 1960. One cannot immediately relate the changes in the Swedish snow climate to the global warming. Estimated values of the correlation coefficient do not even give significant results for the period 1961-2003 despite of the fact that the global mean temperature has raised quite considerably since 1970. The corresponding calculations for the Swedish winter mean temperature show that it plays a very important roll if the precipitation in Götaland and Svealand is coming as rain or snow while it does not matter at all in northern Norrland. / Denna studie är ett resultat av en omfattande undersökning av snöförhållandena i Sverige sedan början av 1900-talet. Jag har för detta ändamål analyserat data av maximala snödjup och antalet dagar med snötäcke per kalenderår från ett 40-tal utvalda stationer. Dessa stationer har sedan delats upp på olika regioner varefter medelvärden har räknats fram i resp. fall. Datamaterialet illustreras här i form av olika stapeldiagram uppdelat på fyra följande kategorier; hela tidsserien, perioden 1961-2003, konsekutiva 10-årsmedelvärden samt en tidsserie med de mest högfrekventa svängningarna bortdämpade. För att kunna bedöma eventuella trender i de uppritade tidsserierna så har jag använt mig av de båda statistiska metoderna enkel linjär regression resp. Mann-Kendall's test. Tillhörande beräkningar redovisas på tabellform. För att svara på frågan om den globala uppvärmningen kan sättas i samband med de senaste 30-40 årens övervägande snöfattiga vintrar i södra Sverige så har jag studerat korrelationen av snödata gentemot det norra halvklotets vintermedeltemperatur. Motsvarande beräkningar av korrelationskoefficienter har också genomförts för den svenska vintermedeltemperaturen Utslaget på testerna visar att det inte har skett så dramatiska förändringar i snöförhållandena på lång sikt. Magnituden på lutningskoefficienten för de anpassade regressionslinjerna tyder på att det maximala snödjupet och antalet dagar med snötäcke i medeltal har legat på en ganska konstant nivå under de senaste hundra åren. När det gäller maximala snödjup så kan man paradoxalt nog se en tendens till en svag uppgång för Götaland och norra Norrland. Det är också de enda fallen som är statistiskt säkerställda för tidsserien som helhet. För den kortare perioden 1961-2003 så kan man däremot se att antalet dagar med snötäcke har minskat relativt kraftigt i södra Sverige motsvarande en nedgång på cirka 40% i Götaland och 20% i Svealand. Test med enkel linjär regression ger signifikanta resultat i båda fallen medan Mann-Kendall endast fastställer trenden för Götaland. En närmare undersökning av det maximala snödjupet för den kortare tidsserien ger dock inte lika tydligt utslag i statistiken men man kan trots allt urskilja en signifikant minskning för Svealand i testet med enkel linjär regression. Det rör sig här om en nedgång på cirka 30% efter 1960. Det går inte att omedelbart relatera förändringarna i det svenska snöklimatet till den globala uppvärmningen. Beräknade värden på korrelationskoefficienten ger inte ens signifikant utslag för perioden 1961-2003 trots att den globala medeltemperaturen har ökat ganska markant sedan 1970. Motsvarande beräkningar för den svenska vintermedeltemperaturen visar att den har väldigt stor betydelse för om nederbörden i Götaland och Svealand faller som regn eller snö medan det för norra Norrland inte har någon nämnvärd påverkan.

This study aims to present and evaluate a new method for measuring the distribution of snow within built-up environments by differencing elevations collected by an Airborne Laser Scanner (ALS) before, and during peak snow accumulation. Few efforts have been made to study the distribution of snow within built-up environments due to the false assumption that high-intensity rainfall is the main contributor to peak yearly runoff rates. Traditional techniques for measuring snow are often difficult to replicate in built-up environments due to incompatibility of methods and barriers such as buildings, roads and private property. Light Detection and Ranging (LiDAR) technology, specifically ALSs, have previously been used to characterize the distribution of snow under forest canopy, and in remote mountain environments. This study investigates and assesses the utility of high resolution, non-intrusive ALS data for estimating the depth and distribution of snow within the town of Uxbridge, Ontario. ALS flights for this study were completed before the onset of snow accumulation, as well as near peak snow accumulation for the winters of 2010 and 2011. Pre and post snow accumulation ALS measured elevations were differenced to estimate the depth of the snowpack across the entire study area at a resolution of 0.5 m. Ground measurements of snow depth were also completed within 24 hours of each of the winter flights. The LiDAR-estimated and ground-measured snow depths were compared using Spearman's rank correlation coefficient as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results from this thesis show that: 1) Snow depths estimated by differencing elevations from two ALS flights show a MAE of 3 cm and an RMSE of 10 cm when compared to ground-measured snow depths. (2) There is a strong, statistically significant relationship (ρ = 0:82, p < 0:001) between LiDAR-estimated and ground-measured snow depths. (3) An average bias of -3 cm was found for the entire dataset showing an underestimation in the LiDAR-estimated snow depths most likely caused by the effects of low lying vegetation on the fall ALS measurements. The results presented in this study demonstrate that ALSs are capable of providing high spatial resolution snow depth estimates within built-up environments. Furthermore, snow depth measurements made using an ALS can be used to increase the current body of knowledge on the distribution and re-distribution of snow within built-up environments. Snow distributions measured by an ALS could also be used for future development and verification of urban hydrological models.

This study aims to present and evaluate a new method for measuring the distribution of snow within built-up environments by differencing elevations collected by an Airborne Laser Scanner (ALS) before, and during peak snow accumulation. Few efforts have been made to study the distribution of snow within built-up environments due to the false assumption that high-intensity rainfall is the main contributor to peak yearly runoff rates. Traditional techniques for measuring snow are often difficult to replicate in built-up environments due to incompatibility of methods and barriers such as buildings, roads and private property. Light Detection and Ranging (LiDAR) technology, specifically ALSs, have previously been used to characterize the distribution of snow under forest canopy, and in remote mountain environments. This study investigates and assesses the utility of high resolution, non-intrusive ALS data for estimating the depth and distribution of snow within the town of Uxbridge, Ontario. ALS flights for this study were completed before the onset of snow accumulation, as well as near peak snow accumulation for the winters of 2010 and 2011. Pre and post snow accumulation ALS measured elevations were differenced to estimate the depth of the snowpack across the entire study area at a resolution of 0.5 m. Ground measurements of snow depth were also completed within 24 hours of each of the winter flights. The LiDAR-estimated and ground-measured snow depths were compared using Spearman's rank correlation coefficient as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results from this thesis show that: 1) Snow depths estimated by differencing elevations from two ALS flights show a MAE of 3 cm and an RMSE of 10 cm when compared to ground-measured snow depths. (2) There is a strong, statistically significant relationship (ρ = 0:82, p < 0:001) between LiDAR-estimated and ground-measured snow depths. (3) An average bias of -3 cm was found for the entire dataset showing an underestimation in the LiDAR-estimated snow depths most likely caused by the effects of low lying vegetation on the fall ALS measurements. The results presented in this study demonstrate that ALSs are capable of providing high spatial resolution snow depth estimates within built-up environments. Furthermore, snow depth measurements made using an ALS can be used to increase the current body of knowledge on the distribution and re-distribution of snow within built-up environments. Snow distributions measured by an ALS could also be used for future development and verification of urban hydrological models.

Snö och permafrost är två interagerande komponenter i Kryosfären. Studien undersöker snödjupets representativitet vid marktemperaturmätningar under snötäcket (BTS) för identifiering av permafrost i Tarfala, norra Sverige. Snödjupsmätningar har utfördes i två korsande 20 m transekter i nordsydlig (NS) samt östvästlig (ÖV) riktning utifrån en BTS-punkt. Totalt har 37 BTS (snödjup &gt; 80 cm) med tillhörande snödjupsmätningar registrerats och analyserats. Snödjupet varierar både lokalt kring mätpunkten och regionalt i mättransekter men är ändå ett representativt snödjup för en punkt. Representativa BTS, sett ur ett snödjupsperspektiv, bör registreras på platser med måttlig snödjupsvariation som på platser med homogent markunderlag, vindskyddade områden, lä bakom ryggar och sluttningar vinkelräta mot den dominerande vindriktningen. BTS provplatser bör också ta hänsyn till de mest förekommande klasserna av parametrarna altitud, sluttning och slutningsriktning för att erhålla representativa BTS. Detta baserat på jämförelse mellan två strategier för insamling av BTS genom permafrostmodellering mellan två dataset. Ett BTS dataset från 2011 jämfört med BTS insamlade mars 2013. / Snow and permafrost are two interacting components in the Cryosphere. This study is focusing on snow depth and its influence on bottom temperature of snow cover (BTS) in Tarfala, Northern Sweden. BTS indicate the absence or presence of permafrost if the snow depth &gt; 80 cm. Snow depth measurements were carried out with a resolution of 1 m in two 20 m crossing transects in NS and EW direction around the BTS point. A total of 37 BTS with accompanying snow depths was measured and analyzed. Snow depths varied around the BTS but are representative for the measured 20 m transects. Locations with moderate snow depth variations make out representative probe sites from a snow depth perspective, such as homogenous ground cover, wind protected areas, in the lee behind ridges and slopes perpendicular towards the dominating wind direction. Also to find representative BTS probe site two strategies for collecting BTS have been evaluated through permafrost modeling. One dataset recorded in 2011 focused on covering a big variety of altitude, slope and aspects. The second dataset registered in March 2013 aimed to cover the most frequent classes of the mentioned parameters. The latter strategy is also the preferably method based on the comparison between the two models.