The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model (MOHYSE), which integrates a snow model (SPH-AV), for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty. This research aims to evaluate the potential of remote sensing data for the characterization of snow and ultimately to develop methods of integration of satellite data in the snow model for the improvement of the simulations of spring floods. Remote sensing snow cover area (SCA) products (MODIS[subscript SCN] & IMS) are compared with snow depth surveys at Environment Canada stations and initial simulations of the models. Thru these comparisons, an effective method of integration (seuil[subscript ÉEN]) of remote sensing SCA products, based on the hypothesis that satellites can not identify small amount of snow because snow become"dirty" and discontinuous, was developed.The improvement of the Nash coefficient and the root mean square error for spring 2004 to 2007 for the simulations with the approach developed compared with streamflow simulated without remote sensing is 0.11 and 21% on the optimized watershed (du Nord) and 0.13 and 22% on the verification watershed (aux Écorces).The method also relies to improve peaks identification as much as 36% on the du Nord watershed and 19% on the aux Écorces watershed.The study also shows the potential of QSCAT data for the characterization of snow cover. Overall accuracies around 90% are obtained for the detection of melt during the month of April from 2001 to 2007 on both studied watersheds.The relation between the rise of the backscatter coefficient and the snow depth surveys shows good correlation for the 2004 to 2006 years for the Lachute and St-Jérôme stations (0.64 to 0.93), but less interesting results for the St-Hippolyte station (0.29 to 0.73). QSCAT products considering only the descendant orbit give best results.The integration of remote sensing albedo product did not allow improvement in the simulations because of holes in the temporal series caused by cloud cover. Also, the relation between fractional snow cover and snow depth did not show interesting results in an operational context.The study shows the interest to create new remote sensing SCA products more precise on the studied region. Future works should also evaluate the possibility to adapt the seuil[subscript ÉEN] method for a Kalman filter approach. A more spatially extensive study and a better comprehension of the backscatter response in microwaves of the different elements might eventually permit to obtain useful results with QSCAT data.

The soil temperature is an essential parameter for the energy balance of the earth. Many methods have been developed to determine summer surface temperature, but the determination in the presence of snow is an ill-conditioned problem since it requires the differentiation of several temperatures (surface of snow, temperature gradient within the snowpack and temperature at the snow/soil interface). Our project was motivated by the need to improve the estimation of soil temperature, within the first centimeters of soil, under the snowpack.The passive microwave remote sensing could provide this information. We showed the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from AMSR-E, version V5) for the estimation of the soil temperature by using a physical multilayer snow model (SNTHERM) coupled with a snow microwave emission model (HUT).The snow model is driven with measurements from meteorological stations (air temperature, precipitation, air relative humidity, wind speed) and data generated by the NARR meteorological reanalysis.The coupled model is validated with in-situ measurements and the retrieved soil temperatures are compared to those derived from the snow model and NARR.The overall root mean square error in the soil temperature retrieval is 3.29 K, which is lower than the error derived from models without the use of remote sensing. This validation must consider the fact that we are comparing temperatures from a point station to that corresponding to an area of 25 x 25 km on the satellite scale. We also show the possibility of mapping the soil temperature. This original procedure constitutes a very promising tool to characterize the soil under snow (frozen or not), as well as its evolution in locations where measurements are unavailable

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.