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.

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.

The South Saskatchewan River Basin (SSRB) is amongst the largest watersheds in Canada. It is an ecologically diverse region, containing Montane Cordillera, Boreal Plains and Prairie ecozones. The SSRB is subject to chinooks, which bring strong winds, high temperatures and humidity deficits that alter the storage of water during winter. Approximately 40% of winter days experience chinooks. Ablation during chinooks has not been quantified; it is not known how much water evaporates, infiltrates or runs off. The aim of this thesis is to characterise the spatial variability of surface water fluxes as affected by chinooks over SSRB subbasins and ecozones. The objectives are addressed using detailed field observations and physically based land surface modelling. Eddy covariance was deployed at three prairie sites. During winter chinooks, energy for large evaporative fluxes were provided by downward sensible heat fluxes. There was no evidence of infiltration until March. The Canadian Land Surface Scheme (CLASS) coupled to the Prairie Blowing Snow Model (PBSM) was used as the modelling platform. A multi-physics version of CLASSPBSM was developed, consisting of two parameterisation options each for sixteen processes. Field observations were used to evaluate each of the configurations. Three parameterisations provide both best snow and best soil water simulations: iterative energy balance solution, air temperature and wind speed based fresh snow density and de Vries’ soil thermal conductivity. The model evaluation highlighted difficulties simulating evaporation and uncertainty in simulating infiltration into frozen soils at large scales. A single model configuration is selected for modelling the SSRB. Modelling showed that the SSRB generally experiences no net soil water storage change until March, confirming field observations. Chinooks generally reduce net terrestrial water storage, largely due to snowmelt and subsequent evaporation and runoff. The Prairie ecozone is that which is most strongly affected by chinooks. The Montane Cordillera ecozone is affected differently by chinooks; blowing snow transport increases during winter and runoff increases during spring. The Lower South Saskatchewan is the subbasin most affected by chinooks. The Red Deer is the subbasin least affected by chinooks.

Le modèle de neige est souvent dépendant du modèle hydrologique avec lequel il est couplé, ce qui peut favoriser la représentation du débit au détriment de celle de la neige. L'objectif est de rendre le calage du modèle de neige plus indépendant de celui du modèle hydrologique en restant facilement utilisable en opérationnel. Dans cette optique, un modèle contraint sur des données d'observations de la neige permettrait d'améliorer d'une part la robustesse des paramètres du modèle de neige et d'autre part la simulation de l'état du manteau neigeux. Dans la première partie de cette thèse, nous avons étudié et modifié le modèle degrés-jour semi-distribué CemaNeige afin qu'il puisse simuler de manière plus réaliste la variable de surface d'enneigement du bassin versant. Cette modification, couplée au calage du modèle sur des données de surface enneigée et sur le débit, a permis d'améliorer la simulation de l'enneigement par le modèle sans détériorer significativement les performances en débits. Nous alors ensuite débuté le développement d'un nouveau modèle de neige à l'échelle ponctuelle. Celui-ci se compose d'un modèle de rayonnements, simulant les rayonnements incidents à partir de données d'amplitude de températures journalières, et d'un modèle de manteau neigeux. Le modèle de manteau neigeux résout les équations de la chaleur au sein du manteau neigeux à l'aide d'une représentation spectrale du profil de température. Cette représentation permet de simuler les profils et gradients de températures en utilisant moins de variables d'état qu'une discrétisation verticale par couches. Pour mieux prendre en compte les mesures ponctuelles de neige, ce modèle devra être distribué. / Snow models are often dependent on the hydrological model they are coupled with, which can promote higher performance on runoff simulation at the expense of snow state simulations performances. The objective of this thesis is to make the calibration of the snow model more independent from the calibration of the hydrological model, while remaining easily usable for runoff forecasting. Calibrating snow model on observed snow data would on one hand improve the robustness of the snow model parameters and on the other hand improve the snowpack modelling. In the first part of this manuscript, we modified the semi-distributed CemaNeige degree-day model so that it can explicitly simulate the watershed snow cover area. This modification coupled with the calibration of the model on snow cover area data and on river runoff data significantly improved the simulation of the snow cover area by the model without significantly deteriorating the runoff performances. Then we started the development of a new point scale snow model. It is based on a radiation model, which simulates incoming radiations from daily temperature range data, and a snowpack model. The snowpack model solves the heat equations within the snowpack by using a spectral representation of the temperature profile. This representation simulates the temperature profile and gradients using fewer state variables than a vertical discretization of the snowpack. In order to be able to use point scale snow observations in the model, it should be distributed on the watershed.

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För att förutsäga snö är en av de viktigaste redskapen en snömodell som beskriver hur snö ackumuleras och avsmälter. En viktig aspekt i snömodellering är variationmed höjden. Höjden påverkar temperatur och nederbörd och därigenom också mönstret för avsmältning och ackumulering.En grad-dag snömodell över området anslutande till Kultsjöns avrinningsområde utfördes med hänsyn till höjdfördelningen. Modellens snötäcke kalibrerades med hjälp av klassificerade satellitfoton över området under perioden mars till juni 2014. Jämförelsen gjordes med hjälp av Cohens Kappa.Resultatet av simuleringen påvisade en påtaglig överrensstämmelse mellan modellen och den observerade data. De simulerade värdena för snödjup jämfördes med observerade data för att utföra en enkel validering. Igen erhölls till stor del överrensstämmelse.Det finns säkert ett behov av tillägg till modellen som tar hänsyn till strålning och vind, då båda dessa faktorer uteblev i modellen. / To predict snow, one of the most important tools is a snow model that describes how snow accumulates and melts. An important aspect in snow modeling is variation with elevation. Elevation influences temperature and precipitation, and therefore also the patterns of snow melt and accumulation.A degree-day snow model over the area around Kultsjön’s catchment area was made with respect to elevation distribution. The modeled snow cover was calibrated using classified satellite photo over the area during the period March to June 2014. The comparison was done using Cohen’s Kappa.The results of the simulation show a large portion of agreement between the model and observed data. The simulated values for snow depth were then compared to the observed data to perform a basic validation. Again there was a large portion of agreement.There is certainly a need for supplementary adjustments to the model that take into account radiation and wind, as both factors were left out of the model.

Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.