Exploring snowpack properties and processes through observation and modelling : case study of the humid boreal forest in eastern Canada

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Exploring snowpack properties and processes through

observation and modelling: A case study of the humid

boreal forest in eastern Canada

Thèse

Achut Parajuli

Doctorat en génie des eaux

Philosophiæ doctor (Ph. D.)

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Exploring snowpack properties and processes through

observation and modelling: A case study of the humid boreal

forest in eastern Canada

Thèse

Achut Parajuli

Sous la direction de:

Daniel Nadeau, Directeur de recherche

François Anctil, Codirecteur de recherche

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Résumé

En raison de l'interception de la neige par la canopée, l'accumulation et la fonte de neige est bien différente en forêt qu’en milieu ouvert. La neige interceptée peut se sublimer, se décharger ou fondre, causant beaucoup de variabilité dans la distribution de l'épaisseur de la neige au sol. La présence d’une canopée modifie également les échanges d'énergie entre la neige, le sol et l'atmosphère. Compte tenu de l'importance de la fonte de neige sur les secteurs dépendants de l’eau tels que la production hydroélectrique, l'approvisionnement en eau agricole et urbaine, il est donc essentiel de surveiller / modéliser les propriétés et les processus du manteau neigeux en forêt.

Le suivi de la neige sur le terrain est une tâche fastidieuse. Ainsi, au fil des ans, plusieurs études ont utilisé des produits satellitaires ou tenté de modéliser les propriétés du manteau neigeux, s’affranchissant ainsi de mesures exhaustives sur le terrain. En général, il existe trois types de modèle de fonte de neige (statistique, à indice de température et par modélisation du bilan énergétique) qui sont utilisés dans une variété de couverts comme les forêts, les glaciers, les milieux ouverts, etc. Ils partagent l’objectif commun de modéliser l'évolution de l'équivalent en eau de la neige. Dans cette étude, nous avons combiné des observations sur le terrain avec des modèles de fonte des neiges dans le but d’atteindre l’objectif global de la thèse, soit de mieux comprendre le comportement de la neige dans un petit bassin versant de la forêt boréale humide. La thèse présente trois objectifs spécifiques : (i) quantifier et modéliser la variabilité spatiotemporelle de la distribution d’équivalent en eau de la neige; (ii) explorer la performance de modèles de fonte de neige à indice de température et (iii) documenter la variabilité spatiale du déficit calorifique du couvert de neige. Chaque objectif spécifique est associé à un chapitre de la présente thèse.

Pour les besoins de cette recherche, nous avons recueilli 1810 échantillons de carottiers à neige, de même que 70 puits de neige, dans 9 sites forestiers distincts d’un bassin expérimental de la forêt boréale humide (Forêt Montmorency; 47°N, 71°O) de l’Est du Canada lors des hivers de 2016-17 et 2017-18. À proximité de ces sites, nous avons fabriqué et déployé des stations mesurant le profil vertical de température de la neige, la température de l'air, l'épaisseur de la neige et le profil de température du sol. Sur ces sites, des données détaillées sur la végétation telles que l'indice de surface foliaire (LAI), la densité du couvert, la hauteur des arbres, la densité des arbres et le diamètre des arbres ont été recueillies à l'aide de mesures sur le terrain et d'un produit LiDAR. Notre analyse a été de plus supportée par les observations de deux tours de flux, nous fournissant ainsi les flux de chaleur sensible et latente entre la surface terrestre et à l’atmosphère à chaque 30 minutes.

Dans le premier chapitre, nous avons mis au jour une relation entre l'épaisseur de neige et le diamètre des arbres environnants. Le site avec une forêt juvénile est celui où la plus grande variabilité spatiotemporelle a été observée. Nous avons utilisé trois modèles statistiques soit la régression linéaire multiple, les arbres de

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régression et les réseaux de neurones (NN) pour identifier les variables pertinentes affectant la variabilité spatiotemporelle de l’équivalent en eau de la neige. Avec un coefficient de Nash de 0,77 en calage et de 0,72 en validation, le modèle NN a présenté les meilleures performances, identifiant ainsi la hauteur de la neige, le diamètre des arbres, l'âge du manteau neigeux et la densité des arbres comme des facteurs clés contrôlant la variabilité spatiotemporelle de la neige en forêt.

Dans le deuxième chapitre, nous avons exploré différents modèles de fonte à indice de température (TI) en s’intéressant à leur performance dans un contexte de données d’entrée rarement disponibles, comme la température de surface de la neige, le rayonnement intrant sous-couvert et la sublimation. Nous nous sommes aussi intéressés à la pertinence de tenir compte de l'interception de précipitation par la canopée et du déficit calorifique du couvert de neige. Sur la base de notre évaluation, à l’exception de la sublimation et de la température de surface qui ont permis de faibles gains de performance, aucun des processus additionnels ou données d’entrée testés n’a généré de gain appréciable de performance.

Enfin, au troisième chapitre, nous avons documenté la variabilité du déficit calorifique de la neige dans quatre sites forestiers à l’aide d’observations récoltées dans des puits à neige. Nous nous sommes également intéressés à la variabilité spatiotemporelle à court terme du déficit calorifique en générant des séries à l’aide d’un approche hybride, basée notamment sur le modèle de surface CLASS (Canadian Land Surface Scheme). Nous avons ainsi pu documenter l'effet de la forêt, de la topographie locale et du régime thermique propre à chaque site sur la variabilité du contenu en froid sur nos sites d'étude. Nous avons entre autres constaté que le contenu en froid était maximal au début février, indépendamment du site, comme c’est là que les températures de l’air étaient les plus froides. Nous avons aussi pu constater qu’en moyenne, 61% du déficit calorifique de la neige était contenu dans les premiers 50 cm.

En résumé, cette recherche s'est concentrée sur l’étude des propriétés du manteau neigeux dans un petit bassin versant de la forêt boréale, à l’aide de mesures exhaustives sur le terrain et en utilisant différents modèles de fonte des neiges. En documentant les processus, nous avons pu mettre en lumière que malgré la présence de couvert forestier aux propriétés contrastées, le couvert de neige présentait de nombreuses similitudes d’un site à l’autre, ce qui est porteur d’espoir pour la modélisation de la neige en forêt.

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Abstract

Because of the interception of snow by the canopy, the accumulation and melting of snow in the forest is different than in the open environment. The intercepted snow can sublimate, discharge or melt, causing a great deal of variability in the distribution of snow depth on the ground. The presence of a canopy also modifies the energy exchanges between the snow, the soil and the atmosphere. Given the importance of snowmelt on water-dependent sectors such as hydroelectric production, agricultural and urban water supply, it is therefore essential to monitor/model the properties and processes of snow cover in the forest.

Monitoring snow in the field is a tedious task. Thus, over the years, several studies have used satellite products or attempted to model snowpack properties, thus avoiding exhaustive field measurements. In general, there are three types of snowmelt models (statistical, temperature index and energy balance model) that are used in a variety of cover types such as forests, glaciers, open environments, etc. They share the common objective of modelling the evolution of snow water equivalent. In this study, we combined field observations with snowmelt models in order to achieve the overall goal of the thesis, which is to better understand the behaviour of snow in a small watershed of the humid boreal forest. This main objective is declined into the three following specific objectives: (i) to quantify and model the spatial and temporal variability of snow water equivalent distribution; (ii) to explore the performance of temperature index snowmelt models; and (iii) to document the spatial variability of the cold content of the snow cover. Each specific objective is associated with a chapter of this thesis. For the purpose of this research, we collected 1810 snow core samples, as well as 70 snow pits, from 9 distinct forest sites in an experimental catchment of the humid boreal forest (Montmorency Forest; 47°N, 71°W) during the winters of 2016-17 and 2017-18. In the vicinity of these sites, stations measuring the vertical snow temperature profile, air temperature, snow depth and soil temperature profile were deployed. At these sites, detailed vegetation data such as Leaf Area Index (LAI), canopy density, tree height, tree density and tree diameter were collected using field measurements and a LiDAR product. Our analysis was further supported by observations from two flux towers, providing us with sensible and latent heat fluxes between the Earth’s surface and the atmosphere every 30 minutes.

In the first chapter, we have highlighted a relationship between snow depth and the diameter of the surrounding trees. The site with a juvenile forest was the one where the greatest spatiotemporal variability was observed. We used three statistical models: multiple linear regression, binary regression trees and neural networks (NN) to identify the relevant variables affecting the spatial and temporal variability of the snow water equivalent. With a Nash coefficient of 0.77 in calibration and 0.72 in validation, the NN model showed the best performance, identifying snow depth, tree diameter, snowpack age and tree density as key factors controlling the spatial-temporal variability of forest snow.

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In the second chapter, we explored different temperature-index (TI) melting models by looking at their performance in the context of rarely available input data such as snow surface temperature, incoming shortwave radiation and sublimation. We also investigated the relevance of taking into account canopy interception and cold content. On the basis of our evaluation, with the exception of sublimation and surface temperature, which resulted in small performance gains, none of the additional processes or inputs tested generated appreciable performance gains.

Finally, in the third chapter, we documented the variability of the snowpack cold content at four forest sites using observations collected from snow pits. We also investigated the short-term spatial and temporal variability of the snowpack cold content by generating series using a hybrid approach, based in part on the Canadian Land Surface Scheme (CLASS) surface model. We were thus able to document the effect of the forest, the local topography and the thermal regime specific to each site on the variability of the cold content at our study sites. Among other things, we found that the cold content was highest in early February, regardless of site, as this is when air temperatures were the coldest. We were also able to observe that, on average, 61% of the snow's heat deficit was contained in the first 50 cm.

In summary, this research focused on studying the properties of the snowpack in a small watershed of the boreal forest, using extensive field measurements and different snowmelt models. By documenting the processes, we were able to highlight that despite the presence of forest cover with contrasting properties, the snow cover showed many similarities from one site to another, which is hopeful for snow modeling in the forest.

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List of Figures

Figure 0.1: Schematic representation of some relevant snow physical processes in forested and open areas. 2 Figure 0.2: Schematic diagram to showcase the difference of snow depth with respect to size of forest gap a) difference of snow depth in relation to tree diameter b) difference in distribution of snow depth with respect to forest gap. ... 3 Figure 0.3: Flow diagram showcasing different types of snow models. ... 5 Figure 0.5: Map of study area showcasing the location of snow profiling stations, snowpit measurement points, tree height, and river network. ... 9 Figure 0.6: Snow profiling station. a) Schematic diagram of snow profiling station b) Photograph of snow profiling station installed at one of the research sites. ... 10 Figure 1.1: Map of the experimental basin (Bassin Expérimental du Ruisseau des Eaux-Volées) focusing on snow-profiling and sites deployed within basins 7 and 7A. Top right: location of the experimental basin in Canada ... 15 Figure 1.2: (a) Schematic diagram of the arrangement of the four subplots dedicated to manual snow sampling. (b) Typical configuration of snow water equivalent sampling location for a given subplot. ... 17 Figure 1.3: Climatic conditions: (a) monthly total precipitation (b) monthly total rain-on snow event based on a 2°C temperature threshold (c) monthly average 2 m air temperatures. The climate normals (1981 - 2010) were extracted from the Forêt Montmorency station (Environment Canada). Monthly total precipitation for W1 and W2 was also extracted from the Environment and Climate Change Canada station and W1 and W2 air temperatures were extracted from average air temperatures measured at Bassin Expérimental du Ruisseau des Eaux-Volées. ... 22 Figure 1.4: Variability of snow water equivalent expressed in terms of the coefficient of variation. The black x-axis labels identify sites while grey x-x-axis labels the subplots. The y-x-axis represents the evolution of time from December 1. The color bar categorises the coefficient of variation. ... 24 Figure 1.5: Inter-site comparison of the snow water equivalent probability density function as a function of the coefficient of variation, and the mean leaf area index (LAI). ... 24 Figure 1.6: Relationship between the average tree diameter and the maximum snow depth and the date of occurrence, for winter 2016–17 (circle) and winter 2017–18 (triangle). ... 25 Figure 1.7: Correlation matrix for the different forest related variables that is, leaf area index (LAI), tree height (TH), tree diameter (Tdia), tree density (Tden), gap fraction (GF) and forest density (FD) used in this study. . 28 Figure 1.8: Graphical representation of the resulting binary regression tree model for estimating snow water equivalent (values inside ovals are in mm) using snow depth (HS in cm), snow duration (SnD in days), tree diameter (Tdia in cm) and tree density (Tden in m−2_{) as independent variables. ... 30} Figure 1.9: Modelled versus observed snow water equivalent using the NN1, NN2, binary regression tree, and multiple linear regression models for training and testing datasets. Solid red lines indicate the 1:1 line and the colour bar represents the density of the scatterplot. NN, neural networks. ... 33 Figure 2.1: Study area. (a) Map of basin 7 within the “Bassin Expérimental du Ruisseau des Eaux-Volées” (BEREV), illustrating the locations of the snow-profiling stations where extensive snow water equivalent (SWE)

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sampling was performed during winters 2016-2017 (W1) and 2017-2018 (W2). Top right of the map: the location of BEREV in Quebec, Canada. (b) Typical winter conditions at the BEREV. (c) Flux tower near site O2. ... 41 Figure 2.2: Flow diagram illustrating the experimental design of this study. ... 44 Figure 2.3: Hourly observed and simulated snow water equivalent (SWE) for the three temperature-index (TI) algorithms. Points illustrate the average SWE and the error bars denote the standard deviation. Note that the curves of algorithms A1 and A2 are almost completely superimposed. ... 50 Figure 2.4: Performance algorithms A1 to A3 in terms of the RMSE (mm w.e.) and Pbias (%). The colours of the bars denote absolute values. ... 51 Figure 2.5: Observed versus simulated timing of the disappearance of the snowpack (days) for algorithms 1 to 3. Negative and positive values indicate anticipation and lag, respectively. The colours of the bar denote absolute values. ... 52 Figure 2.6: Importance of some relevant inputs: water vapour losses to precipitation ratio at daily time steps (a) for the first winter (W1) (b) for the second winter (W2) (c) Air (Ta) and snow surface (Ts) temperature (all sites averaged) at daily time steps (c) for the first winter (W1) and (d) air (Ta) for the second winter (W2). In the above plot, the shaded region represents the ablation period and to its left is the accumulation period. ... 53 Figure 2.7: Hourly observed and simulated SWE for tests 1 to 6, using A1 as the base model. F_R indicates for forest and radiation, Int is snow interception, Ts is snow surface temperature, Sub is sublimation, and CC is cold content. The necessary inputs (snow depth and Ts) for test 5 were available for W2 only, limiting our test. Points illustrate the average SWE and the error bars denotes the standard deviation. ... 54 Figure 2.8: Model performance expressed in terms of the RMSE (mm w.e.) and Pbias (%) for all tests. F_R indicates forest and radiation, Int is snow interception, Ts is snow surface temperature, Sub is sublimation, and CC is cold content. The results are given separately for W1 and W2. The colours of the bar denote absolute values. ... 55 Figure 2.9: Snow depth recorded at the three mature sites (M1, M2 and M3) during winter 2. The corresponding leaf area index (LAI in m2_m−2_{), canopy cover (Csc in fraction), and mean tree height (TH in m) are also provided.} ... 57 Figure 2.10: Effects of precipitation uncertainty (amount and phase) on modelled SWE at hourly time steps using the best-performing TI model from Phase II (with sublimation), at sites O1 (left) and M2 (right). Top panel: Modelled SWE using bias corrected vs. uncorrected precipitation data. Bottom panel: Impacts of various phase partitioning approaches. Four approaches are tested: a) the linear partitioning of rain and snow between air temperatures of 0 to 2 °C; b) the linear partitioning of rain and snow between air temperatures of −1 to 3 °C; c) snow and rain below and above 0 °C; d) snow and rain below and above 2 °C. Blue dots stand for the mean SWE, whereas the error bars denote the standard deviation. ... 60 Figure 3.1: Study area: a) Basin 7 within BEREV illustrating the location of the four study sites (A1 to A4), where snowpit samples were collected and snow-profiling stations installed, b) location of BEREV in eastern Canada, c) snow-profiling station installed at site A1, and d) typical winter conditions at BEREV as seen from the flux tower at site A2. ... 66 Figure 3.2: (a) Daily 2-m air temperature (Ta) for all study sites. (b) Daily 2-m wind speed (um) for sites A1 (sapling) and A2 (juvenile). The shaded region depicts the period during which extensive snowpit measurements were collected. Coloured dot illustrates a specific snowpit survey. Spring melt started on 21 April 2018. ... 70

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Figure 3.3: Weekly CC observations. The shade illustrates the active spring melt. ... 71 Figure 3.4: Weekly snowpack total CC. The shade illustrates the spring melting period. ... 72 Figure 3.5: Fraction of the total CC contained in the top (upper 40 cm), bottom (lower 30 cm) and the middle layer (remainder) layers, as well as snowpit depth. The shaded region illustrates the spring melt period. ... 73 Figure 3.6: Observed versus CLASS bulk values of SWE, snow density, and CC. ... 74 Figure 3.7: Observed versus modelled snow density and CC derived utilizing empirical formulations described in section 3.4.2.2 across the four sites of interest. The layers of the snow cover are aggregated into three classes: top (upper 40 cm), bottom (lower 30 cm) and middle (remainder). ... 75 Figure 3.8: Performance of CC and density simulation following Andreadis et al. (2009), coefficient of determination (R2_{, left) and percent bias (Pbias, right). The colour of the bars for Pbias denotes absolute values.} ... 75 Figure 3.9: 30-min 10-cm CC simulations. Light green shades are rain events, while light blue shade depicts the melting period. ... 76 Figure 3.10: 10-cm snowpack temperature (top plot) and density (bottom plot) observations at site A1. Shaded illustrates melting. ... 77 Figure 3.11: Observed versus modelled snow density (first 10 cm). ... 79

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List of Tables

Table 1.1: Basin characteristics in the vicinity of the nine study sites... 16 Table 1.2: List of possible model inputs. ... 19 Table 1.3: Precipitation, snow cover duration, number of rain-on-snow events derived using field observations and a 2°C temperature threshold and field measured maximum snow depths (site with maximum snow depth). ... 22 Table 1.4: Identification of best possible combination for the NN1 model using NSE evaluation criterion, where “ref” stands for the retained input candidates... 27 Table 1.5: Testing the NN2 model using 6 input candidates and 5 input MLPs ... 28 Table 1.6: Performance evaluation criteria for the different snow models used in the study area sorted in descending order of performance ... 32 Table 1.7: NSE values for the temporal (T1 and T2) and spatial transposability tests (S1). ... 33 Table 1.8: Performance of NN model based on different evaluation criteria (R2_{, MAE (mm w.e.), RMSE (mm} w.e.), Pbias (%), NSE) utilizing leave one out cross-validation approach with respect to sites. Here cal represents the training efficiency, val represents the testing efficiency and n represents the number of

samples. ... 34 Table 2.1: General forest characteristics and aspects for the study sites, where LAI is the leaf area index and Csc is the canopy density (1 is a closed canopy and 0 is an open site). ... 42 Table 2.2: Set of parameters used in this study ... 48 Table 2.3: Metrics for the best-performing TI model from Phase II (with sublimation) considering precipitation undercatch and precipitation phase comparison. BC = bias corrected, UC = uncorrected precipitation. ... 59 Table 3.1: General canopy characteristics of the four experimental sites ... 66 Table 3.2: Local availability of CLASS inputs. ... 68 Table 3.3: Peak total CC, date of occurrence, snow depth, tree height, maximum snow depth, and mean total CC over a period of 15 weeks. ... 72

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Abbreviation

∆T : difference between maximum and minimum temperatures

a : aspect

BC : bias-corrected

BEREV : Bassin Expérimental du Ruisseau des Eaux-Volées

bf : melt factor for forested areas

bhbv : conversion factor to change ◦ (slope/aspect) to mm °C−1 day−1

BRT : binary regression tree

CC : cold content

ci : specific heat of ice

CLASS : Canadian Land Surface Scheme

CP : control value

CRm : snow compaction

CRo : compaction due to the weight of overlying snow

Csc : canopy cover

CV : coefficient of variation DEM : Digital Elevation Model

DFIR : Double Fence Intercomparison Reference

Ds : snow depth

EB : energy balance

ECCC : Environment and Climate Change Canada

ɛa : atmospheric emissivity

f : empirical compaction coefficient

FD : forest density

g : acceleration due to gravity

GF : gap fraction

h : hour

HBV : Hydrologiska Byråns Vattenbalansavdelning

HS : snow depth

I : snow interception

I* : maximum canopy load

I1 : snow interception that occurs before the unloading phenomenon Kn : attenuated solar radiation beneath the canopy

L0 : initial intercepted snow load from the end of the previous time step

LAI : leaf area index

LiDAR : Light Detection and Ranging

Lo : longwave radiation

m ASL : meters above sea level

M : potential snow and ice melt at an hourly time step MAE : mean absolute error

mf : melt factor

mft : base melt factor

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MLP : multilayered perceptrons

Mod : modelled

MRL : multiple linear regression

mtd : time-variable melt factor

n0 : viscosity

NN : neural network

NSE : Nash-Sutcliffe efficiency

Obs : observed

P : snowfall amount

Pbias : percent bias

Ps : load pressure for each layer

Qe : convective exchange of latent heat

Qg : ground heat conduction

Qi : change in snowpack internal sensible and latent energy

Qm : loss of latent heat of fusion due to meltwater leaving snowpack

Qn : convective exchange of sensible heat with the atmosphere

Qnl : net longwave radiation energy

Qns : net shortwave radiation energy

Qr : rainfall sensible and latent heat flux

R2 _{: coefficient of determination}

RCPs : Representative Concentration Pathways

rf : radiation factor

RMSE : root mean square error

S : slope

S : species coefficient

SL : single-layered snowpack

SnD : snowpack age

SONDAS : spatial resolution via snow data assimilation SSA : specific surface area

SWE : snow water equivalent

t0 : start of daylight

t1 : end of daylight on the current day

Ta : air temperature

td : current timestep of the day

Tden : tree density

Tdia : tree diameter

TH : tree height

TI : temperature-index

Tm : melting temperature

Tn : tree sampled

Ts : snow surface temperature

Tsp : snowpack temperature

Tt : threshold temperature

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u : wind speed

UC : uncorrected precipitation

um : wind speed

W1 : winter of 2016-17

W2 : winter of 2017-18

Wns : amount of new snow

Ws : snow within the snowpack layer

Z : factor ensuring the daily mean value of mtd is equal to mf

β : factor to convert temperature amplitude into the melt factor

ρl : bulk density of liquid water inside the snowpack

ρsa : snowpack density

ρsf : fresh snow density

ρw : density of water

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Remerciements

Je voudrais exprimer ma profonde gratitude aux personnes et organisations qui m'ont aidé à mener cette recherche et à améliorer la qualité globale de ma thèse de doctorat. Tout d'abord, je tiens à remercier mon directeur de recherche, le professeur Daniel Nadeau, pour m'avoir supervisé et aidé à développer et améliorer cette recherche. Deuxièmement, je voudrais remercier mon codirecteur, le professeur François Anctil, qui a fourni des commentaires constructifs et soutenu mes activités de recherche. Je voudrais également remercier le professeur Sylvain Jutras qui m'a fourni les équipements et capteurs nécessaires qui m'ont énormément aidé à mener ma campagne de mesures sur le terrain. Je tiens également à remercier les membres du jury, dont le professeur Christophe Kinnard, le professeur Pierre Bernier et le professeur Tadros Ghobrial, pour leurs commentaires constructifs afin d'améliorer la qualité globale de cette thèse.

Les mesures hivernales sur le terrain pour cette étude ont été possibles grâce au professeur André Desrochers, qui nous a gentiment donné accès aux motoneiges. J’aimerais remercier François Larochelle et Martine Lapointe pour leur soutien initial dans la conception des instruments. J’exprime ma profonde gratitude à Annie-Claude Parent qui a géré la logistique, participé aux campagnes sur le terrain et soutenu ma recherche du début à la fin. Merci à Benjamin Bouchard et Médéric Girard, qui ont tous deux contribué aux campagnes de terrain hivernales. Le personnel de la Forêt Montmorency, dont Robert Côté et Charles Villeneuve, sont aussi remerciés pour leur généreux soutien et leur immense aide logistique. Je veux aussi remercier Fabien Gaillard Blancard, Jonas Götte, Kelly Proteau, Bram Hadiwijaya, Pierre-Erik Isabelle, Oliver Schilling, Judith Fournier, Alicia Talbot Lanciault, Georg Lackner, Carine Poncelet, Amandine Pierre, Guillaume Hazemann, Marco Alves, Adrien Pierre, Antoine Thiboult et les membres du groupe PEGEAUX qui ont aimablement participé aux campagnes de terrain hivernales.

J’aimerais souligner la contribution financière du Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), d’Ouranos (consortium sur la climatologie régionale et l'adaptation aux changements climatiques), d’Hydro-Québec, d’Environnement et Changement climatique Canada et du ministère de l'Environnement et de la Lutte contre les changements climatiques.

Je tiens à saluer les efforts de ma femme Sheetal Rimal qui était à mes côtés dans ce long marathon de doctorat. Merci Aviyan Parajuli, mon bébé qui m'a toujours souri et a fourni une énergie positive pour terminer ma thèse de doctorat. Je voudrais exprimer ma gratitude à ma famille de retour au Népal qui m'a motivée à terminer mon doctorat. Je tiens également à remercier les amis du Québec, particulièrement Michèle Legault et sa famille qui ont été très solidaires, ont célébré ensemble le festival népalais et Québécois.

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Avant-propos

Cette thèse est composée de six sections rédigées en anglais. La thèse débute par l’introduction générale, qui est suivie de trois chapitres sous la forme d'articles scientifiques, d’une section présentant les perspectives de recherche et enfin d’une conclusion générale. En tant qu'auteur principal, je suis responsable des différents aspects de la recherche qui sont présentés dans cette thèse, y compris la conception de la recherche, les simulations de modèles, les mesures sur le terrain, l'analyse des données, la préparation d'articles ainsi que leur soumission à des revues internationales. Pour améliorer la conception de ma recherche, la sélection de modèles, la préparation d'articles de recherche et la thèse globale, mon directeur, le professeur Daniel F. Nadeau et mon codirecteur, le professeur François Anctil ont fourni d'importantes suggestions et recommandations. Le professeur Sylvain Jutras a fourni les capteurs nécessaires pour le déploiement sur le terrain, a fourni les commentaires nécessaires pour améliorer le dispositif expérimental et les articles scientifiques. Des membres de notre groupe de recherche dont Annie-Claude Parent, Benjamin Bouchard et Médéric Girard ont participé à la collecte de données sur le terrain et ont contribué à la préparation du premier article. Oliver S. Schilling a également participé au suivi sur le terrain et contribué à la préparation du deuxième article. Marco Alves a contribué au troisième article en exécutant les simulations CLASS.

Des informations supplémentaires mentionnées dans les principaux chapitres de cette thèse se trouvent dans la section Annexe. Celle-ci comprend également des informations sur deux autres publications où j’ai agi à titre de co-auteur. Il convient également de noter que les chapitres I, II et un article de recherche supplémentaire en tant que co-auteur ont déjà été publiés. Pour sa part, le chapitre III est en phase finale de préparation avant sa soumission à une revue internationale et un autre manuscrit où j’ai agi comme co-auteur est en cours de révision dans la revue Water Resources Research.

Chapitre 1

Parajuli A, Nadeau DF, Anctil F, Parent A-C, Bouchard B, Girard M, Jutras S. 2020a. Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling. Hydrological Processes 34 (11): 2628–2644 DOI: https://doi.org/10.1002/hyp.13756

Cet article a été soumis le 17 juin 2019, accepté le 18 mars 2020 et publié le 18 mai 2020. Chapitre 2

Parajuli A, Nadeau DF, Anctil F, Schilling OS, Jutras S. 2020b. Does data availability constrain temperature-index snow model ? A case study in the humid boreal forest. Water 12 (8): 1–22 DOI: 10.3390/w12082284

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Cet article a été soumis dans le numéro spécial Whither Cold Region Hydrology under Changing Climate

Conditions de la revue Water. Il a été soumis le 18 juin 2020, accepté le 12 août 2020 et publié le 14 août 2020.

Chapitre 3

Parajuli A, Nadeau DF, Anctil F, Jutras S, Alves M. Distributed observations and estimation of snowpack cold content in a boreal coniferous forest.

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Introduction

Climate change is posing a notable global threat to environmental and economic sustainability (Shrestha et al., 2017). Such change, particularly an increase in surface temperature, significantly perturbs the hydrological dynamics of snow and ice fed catchments (Barnett et al., 2005; Poulin et al., 2011; Peng et al., 2017). Recently, Arheimer et al. (2017) implemented an ensemble climate model having 18 members (with RCPs 4.5 and 8.5) to quantify the impact of climate change on snow-fed streams. The authors predicted the redistribution of river flow by 19% and the reduction of peak flow by 5% at the end of the 21st_{century, due to reduced snow storage mostly} induced by global warming. Thus, the cryosphere acts as a sentinel to climate change (Peng et al., 2017), and is often used in impact studies of the hydrological and water resource sectors (Yao et al., 2018). In terms of areal extent, snow by far dwarfs any other cryospheric components (Walker and Goodison, 2000). As an example, during peak winter, up to 40% of the land surfaces within the Northern Hemisphere can be covered by snow (Langlois et al., 2009). On top of it, approximately 19% of snow cover overlays with forest (Rutter et al., 2009; Musselman et al., 2012).

Forest canopies interact with the incoming snowfall by partly holding the snow before it reaches the surface, a process referred to as snow interception (DeWalle and Rango, 2008). As a result, we typically observe reduced snow accumulation beneath the canopy than in forest openings (Hedstrom and Pomeroy, 1998; Molotch et al., 2007; Gustafson et al., 2010) thereby resulting the difference in overall energy distribution (Figure 0.1). The presence of a forest canopy alters the exchange of atmospheric fluxes and incoming precipitation, which determines the snow accumulation and melt dynamics (Musselman and Pomeroy, 2017). For instance, Molotch

et al. (2007) explored the role of canopy interception on below canopy sublimation rates at Niwot Ridge Forest,

Colorado, USA. The authors reported smaller subcanopy sublimation induced by larger within-canopy latent heat flux. Thus, snow dynamics beneath the canopy largely depends on snow interception (Pomeroy et al., 1998b). The density of the canopy determines the availability of shortwave radiation and longwave radiation, which is typically mostly driving the snowmelt.

In Canada, the boreal forest is the dominant land cover, and mostly consists of evergreen conifer species having needle-like leaves. For this type of canopy, snow is held at the base of needles and creates small bridges across them (Figure 0.1), which eventually cover the entire branch (DeWalle and Rango, 2008). As such, evergreen conifer trees tend to intercept more snow in winter than do deciduous trees (Pomeroy et al., 1998a). This intercepted snow can stay from several days to a few months in cold boreal forests (Pomeroy and Schmidt, 1993), and can even never reach the ground surface if it sublimates (Figure 0.1).

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Figure 0.1: Schematic representation of some relevant snow physical processes in forested and open areas. Apart from sublimating, intercepted snow can also be unloaded, drip down to the ground or melt (Rutter et al., 2009), resulting in a highly heterogenous distribution of snow depths within forests. Moreover, it is equally important to understand the role of forest gaps on snow accumulation and melt dynamics. As portrayed in Figure 0.2, several studies have highlighted the relationship between forest gaps and snow dynamics (e.g. Murray and Buttle, 2003; Sun et al., 2018). These studies attested that the forest with small-size gap have the ability to retain more snow due to reduced snow interception, wind and solar shelter, and reduced longwave radiation. For instance, Dickerson-lange et al. (2015) carried out a study in the western slope of Cascade Range in the Pacific Northwest by relating canopy gap size with snow duration. The authors reported longer persistence of snow (1 to 2 weeks) at the circular forest gap cut having a diameter of 20 m than at the non-exploited forest site. In a similar study, Golding and Swanson (1978) and Sun et al. (2018) reported longer retention potential of snow by numerous small distributed gaps than few larger ones having the same area. For instance, Varhola et al. (2010) reported the increase in snow retention potential with the increase in gap size until the certain threshold of forest

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gap. The authors concluded that after attaining such threshold of forest gap, the snow retention potential then decreases with the increase in forest gap size due to enhanced solar radiation penetration.

Figure 0.2: Schematic diagram to showcase the difference of snow depth with respect to size of forest gap a) difference of snow depth in relation to tree diameter b) difference in distribution of snow depth with respect to forest gap.

Spatiotemporal variability in snow distribution

One of the key variables for the study of snow in the forest is the snow water equivalent (SWE), defined as:

𝑆𝑊𝐸 = 𝜌𝑠

𝜌𝑤 𝐻𝑆 (0.1)

where, ρs (kg m−3) is the snow density, ρw (kg m−3) is the water density, and HS (m) is the snow depth.

Spatiotemporal patterns of forest SWE are in part controlled by the variability in the canopy structure, resulting from natural and anthropogenic disturbances, including forest management, forest fires, diseases and infestations. Other sources of variability include the climate regime (Winkler and Moore, 2005), slope aspect and elevation (Murray and Buttle, 2003; López-Moreno and Stähli, 2008; Roth and Nolin, 2017), solar radiation (Molotch et al., 2005; Musselman et al., 2008; Mazzotti et al., 2019) and wind speed (Woods et al., 2006; Broxton

et al., 2015).

In order to describe the spatial variability of SWE, observations must be collected. Those taken in situ are particularly difficult, as frequent visits are required to sites that are sometimes difficult to access. An alternative is therefore to use remote sensing products. For instance, daily gridded snow products informing on snow depth

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and snowmelt are now available at 1 km2_{spatial resolution via Snow Data Assimilation (SNODAS) (Clow et al.,} 2012), but unfortunately this resolution is insufficient to capture small-scale variability. Another alternative that has recently emerged is the use of LiDAR data, that allows for a detailed description of the spatial difference in snow depth distribution. This technology has been successfully used in the forest environment (Broxton et al., 2019; Mazzotti et al., 2019), and can work well even in remote areas (Podgórski et al., 2018). For LiDAR surveys, the sensors are embarked on board of a flying device (airborne platform or drone), and will then collect raw point cloud data with a laser scanner (Podgórski et al., 2018; Broxton et al., 2019). Such datasets could be utilized to derive canopy variables such as leaf area index (LAI), canopy density, tree height, and snow interception estimates (Moeser et al., 2015; Mazzotti et al., 2019). Recently, Broxton et al., (2019) utilized these laser scanners to derive snow depth maps from highlands of central Arizona, USA. The authors reported necessity to bias correct snow depths particularly for shallower snow and for sites with low vegetation cover. Obviously, validation of such datasets still requires detailed field-based measurements.

Types of snow models

Depending on the data availability and the desired degree of complexity, SWE can be simulated using a wide range of models. These can be grouped into three main categories (Figure 0.3): empirical models (Molotch et

al., 2005; Musselman et al., 2008; Jonas et al., 2009; McCreight and Small, 2014), temperature-index model

(Jost et al., 2012; Parajuli et al., 2015; Bouamri et al., 2018; Nemri and Kinnard, 2020) and physically-based energy balance models (Andreadis et al., 2009; Ellis et al., 2010; Shrestha et al., 2010; Mahat and Tarboton, 2012; Vionnet et al., 2012; Gouttevin et al., 2015; Jennings et al., 2018a).

Empirical models

Empirical models approximate SWE by relying of commonly available driving variables. Some of these models use snow density and air temperature (e.g. Pomeroy et al., 1998; Meløysund et al., 2007), while others are based on snow depth alone or combined with the period of the year and elevation (Jonas et al., 2009, Sturm et al. 2010). Other empirical models will exploit existing relationships between topographic features (Balk and Elder, 2000; Erxleben et al., 2002; López-Moreno et al., 2010), local meteorological conditions (Molotch et al., 2005; Musselman et al., 2008), forest variables (Talbot et al., 2005; Jost et al., 2007; Musselman et al., 2008) and SWE.

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Figure 0.3: Flow diagram showcasing different types of snow models.

Temperature-index models

Temperature-index models rely on air temperature to derive the snow and ice melt (Hock, 2003; Pradhananga

et al., 2014; Valéry et al., 2014a; Kayastha et al., 2020). In short, melting is activated if the air temperature

exceeds a predefined threshold. This melting is proportional to the difference between the observed air temperature and the threshold. TI models are regarded as an integral component of hydrological models (Bouamri et al., 2018; Nemri and Kinnard, 2020). Such models are often implemented in remote locations due to their simplicity and limited data requirements.

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Energy balance models

With the energy budget method, the purpose is to estimate snowmelt considering all the fluxes within the snowpack. The energy budget method as outlined in DeWalle and Rango (2008) is given as:

𝑸𝒊= 𝑸𝒏𝒔+ 𝑸𝒏𝒍+ 𝑸𝒏+ 𝑸𝒆+ 𝑸𝒓+ 𝑸𝒈+ 𝑸𝒎 (0.2)

where, Qi is the change in snowpack internal sensible and latent energy, Qns the net shortwave radiation energy,

Qnl the net longwave radiation energy, Qn the convective exchange of sensible heat with the atmosphere, Qe the

convective exchange of latent heat, Qr the rainfall sensible and latent heat flux, Qg the ground heat conduction,

and Qm the loss of latent heat of fusion due to melt-water leaving snowpack. In the above equation, all the terms

are in W m−2_{. To estimate snowmelt rates, each term within equation (0.2) thus needs to be either measured or} estimated. Energy balance models are often implemented as multilayer snow schemes and need to include the description of internal snowpack processes, such as the cold content. Examples of such models are CROCUS (Vionnet et al., 2012; Gouttevin et al., 2015) and Snowpack (Lehning et al., 2002; Jennings et al., 2018a). The cold content of a given snowpack is the amount of energy required to raise the snowpack temperature to reach an isothermal condition at 0°C, and is defined as:

𝑪𝑪 = 𝒄𝒊𝝆𝒔𝑯𝑺 (𝑻𝒔𝒑− 𝑻m) (0.3)

where, CC is cold content (MJ m−2_{), c}_i_{is the specific heat of ice (2.1 × 10}−3_{MJ kg}−1_°C−1_{), ρ}_s_{is the snow density} (kg m−3_{), HS is the snow depth (m), T}_m_{is the melting temperature (°C) and T}_s_{is the snowpack temperature (°C).} It is difficult to extract relevant variables (snow density, snow depth and snowpack temperature) for direct estimation of cold content.

Research gaps

In forested environments, spatiotemporal variability of snow creates substantial difficulties when modelling the SWE. Due to such heterogeneity, it is difficult to monitor snow and associated processes which deems either labour-intensive field monitoring from representative locations or remote sensing products. Hence over time, to understand the snow dynamics, a variety of modelling approaches have been proposed, ranging from simple temperature index models to energy balance approaches by combining field measurement and remote sensing products. Due to the complexity of the processes involved and the limited validation datasets available, snow modelling in the forest (especially in the boreal forest, as it is remote) faces significant challenges. Efforts are therefore required to document the spatial variability found there, looking at the causes and different avenues of statistical modelling. Nonetheless, the presence of spatiotemporal variability combined with measurement errors creates stiff challenge when upscaling the point-scale measurement into catchment level analysis. For

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operational applications and TI models, it seems relevant to explore whether there are input variables other than air temperature that would allow us to better represent the processes and thus obtain interesting performance gains. Moreover, TI models provide ad-hoc parameter to derive reasonable estimate at particular location and often requires calibration when tested in other locations. Finally, it seems crucial to investigate snowpack state variables until then little documented, such as the cold content, which controls the onset of melting, to help better understand processes and support model development.

Research objectives

The main objective of this study is to analyse the snow properties and processes through observation and modelling over a small-forested catchment representative of the boreal zone. This main goal is declined into three specific objectives:

1) To explore the spatiotemporal variability of the snow water equivalent in relation with the canopy structure and topographic characteristics through observation and modelling.

2) To test predictive ability of temperature-index model with insertion of relevant inputs/variables in a small boreal forested catchment.

3) To explore the cold content variability in four distinct forest stands relying on in-situ field observation and estimations.

Figure 0.4 illustrates presents the links between the different specific objectives and, by the same occasion, proposes an overview of the thesis. Here the snow observations (snow pit survey and snow tube samples) enable us to quantify the spatiotemporal variability of SWE, undertake tests to the existing type of snow-melt modelling, and understand the variability of cold content across our forest research sites.

Figure 0.4: Schematic diagram showcasing the relationship between the three specific objectives.

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For our first objective, supported by intensive field campaigns and high-resolution LiDAR observations, key forest and topographical variables inducing the spatiotemporal variability of SWE are explored through the lens of snow observations and statistical modelling approaches.

As a second specific objective, a widely used temperature-index model is tested when driven relevant inputs affecting the snow accumulation and melt, such as observed sublimation, snow surface temperature and cold content.

Supported by numerous and detailed snow-pit measurements, the third specific objective is interested in quantifying the variability of a key state variable that affects the snow-melt rate and timing, i.e., snowpack cold content, across four distinct forested sites. This study also documents the performance of a physically-based land surface model in reproducing some of the properties of the snow cover.

Methods

Study Area

Our study was conducted in the “Bassin Expérimental du Ruisseau des Eaux-Volées” (BEREV), an experimental watershed located in the Montmorency Forest, Quebec, Canada (Figure 0.5).

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Figure 0.4: Map of study area showcasing the location of snow profiling stations, snowpit measurement points, tree height, and river network.

Located in a humid boreal forest, this region experiences a cold continental climate with substantial amounts of solid precipitation (633 mm in water equivalent, based on the 1981-2010 climate normal – see Isabelle et al., (2020)). The minimum, maximum and median annual flows measured at the outlet of basin 7 from years 1965 to 2013 are 0.008, 3.52 and 0.062 m3 _s−1_{respectively. This region sits in a U-shaped valley with glacial deposits.} Snow typically covers the land surface of the BEREV from early November to mid-May. Coniferous tree species such as balsam fir are found predominantly with some occurrences of other species such as white birch, white spruce and black spruce.

Manual snow sampling design

To achieve our research objectives, we have selected 9 research sites within BEREV. The exact selection of sites is a delicate process. On the one hand, it is impossible for logistical constraints to have a very high number of sites. On the other hand, enough sites must be available to ensure spatial representativeness of the characteristics of the territory, such as tree height, altitude, etc. In order to choose the sites, we adopted a nested stratified sampling design, consisting of the 9 distinct forest sites (three elevation bands, variable aspects and forest cover), having 29 strata. Among these strata, 24 strata were dedicated to extract snow cores, for a total collection of 1810 samples (snow depth and density) spanned across the two winters of study (2016-17 and 2017-18). From these strata, a minimum of 5 and a maximum of 12 snow samples were extracted on a weekly or biweekly basis, depending on the site conditions. The accessible sites (1,2,4,5,8,9) were subject to weekly measurements, while samples were collected every other week at the remote sites (3,6,7). From the remaining 5 strata, we collected 70 snowpit samples (snow depth, density profile, temperature profile) on a weekly basis during the two winters. Due to the occasional harsh climatic conditions and various technical problems, some of the planned visits could not be carried out.

Snow profiling stations

At each of the nine study sites, a custom-made snow profiling station was installed nearby the sampling strata. The snow-profiling station consisted of 18 T-type thermocouples (10 cm apart from ground surface) measuring the snow temperature profile (Figure 0.6). An additional T-type thermocouple was installed on top of the snow-profiling stations and enclosed within a radiation shield, thereby measuring the air temperature at each site. A snow depth sensor (Ultrasonic depth sensor - Judd Communication, USA and SR50 - Campbell Scientific, UT, USA) and three soil temperature sensors (107 BAM, Campbell Scientific, UT, USA) were part of snow-profiling station, which measured the height of the snowpack and the soil temperature profile (10 cm apart).

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Figure 0.5: Snow profiling station. a) Schematic diagram of snow profiling station b) Photograph of snow profiling station installed at one of the research sites.

Supporting dataset

Our research was conducted in a boreal forest having variable stand structure. Therefore, we collected canopy information such as tree density and height of canopy by conducting a detailed vegetation analysis. We followed a standard protocol to carry out the analysis at each site, thereby selecting a circular plot having an area of 400 m2_{. Leaf area index (LAI) and gap fraction datasets were extracted from each stratum using a hemispherical} camera system (WinSCANOPY, Regent Instruments Inc.). The measurements of tree height, tree density, LAI, and gap fraction were collected during the summer of 2017. Our analysis was supported by LiDAR data, which was collected at 1-m resolution during the snow-free conditions in 2016, thereby helping us to extract relevant vegetation metrics such as canopy density, tree height and topographic information. Our research also featured observations by two flux towers monitoring the flux of heat and water vapour with the eddy covariance technique (IRGASON, Campbell Scientific, UT, USA) at 30 min time steps. The flux towers were also equipped with net radiometers (CNR4 net radiometer, Kipp and Zonen, Delft, the Netherlands) and wind speed and direction sensors (R.M. Young Company, USA). Precipitation data was extracted from the Environment and Climate Change Canada (ECCC) station (ID: 7042388), located some 4 km north of our research site (BEREV).

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Chapter 1 Exploring the spatiotemporal variability of

the snow water equivalent in a small boreal forest

catchment through observation and modelling

1.1 Résumé

Dans les bassins versants des régions nordiques, il est crucial de faire le suivi et la modélisation de l'équivalent en eau de la neige (EEN), en particulier en période de fonte. La distribution de l’EEN peut cependant être très hétérogène, en particulier dans les environnements forestiers. Dans ces régions, peu d'études ont exploré la variabilité spatiotemporelle de l’EEN en relation avec les caractéristiques de la végétation. L'objectif de cet article est de combler cette lacune, grâce à un suivi détaillé à neuf endroits dans un bassin versant forestier de 3,49 km2_{du sud du Québec, Canada (47°N, 71°O). Le bassin versant reçoit une moyenne annuelle de 633 mm de} précipitations solides et est principalement recouvert de peuplements de sapins baumiers. Appuyée par une campagne intensive sur le terrain et de données LiDAR haute résolution, cette étude explore l'effet des caractéristiques forestières à petite échelle (hauteur des arbres, diamètre des arbres, densité du couvert, indice de surface foliaire (LAI), densité des arbres et fraction d'écart) sur la variabilité spatiotemporelle de la distribution du SWE. Un plan d'échantillonnage aléatoire stratifié imbriqué a été adopté pour quantifier la variabilité à petite échelle dans le bassin versant et 1 810 échantillons de neige manuels ont été recueillis au cours des hivers de 2016-17 et 2017-18. Cette étude a exploré la variabilité de l’EEN en utilisant des coefficients de variation (CV) et en relation avec le LAI. Nous présentons également les différences spatiotemporelles existantes de l'épaisseur maximale de la neige entre les différents peuplements et leur relation avec le diamètre moyen des arbres. En outre, en exploitant les principales caractéristiques de la végétation, cet article explore différentes approches pour modéliser l’EEN, telles que la régression linéaire multiple, l’arbre de régression binaire et les réseaux de neurones (NN). Au final, nous n’avons pas pu établir de relation entre le CV de l’EEN et le LAI. Cependant, nous avons observé une augmentation de l’épaisseur maximale de la neige avec la diminution du diamètre des arbres, suggérant une association entre ces variables. La modélisation NN (efficacité de Nash-Sutcliffe = 0,71) a révélé que l’épaisseur de la neige, l’âge du manteau neigeux et les caractéristiques de la forêt (diamètre et densité des arbres) sont des variables de contrôle clés sur l’EEN. En utilisant uniquement des variables jugées plus facilement disponibles (épaisseur de la neige, hauteur des arbres, âge du manteau neigeux et altitude), la performance NN ne diminue que de 7% (NSE = 0,66).

1.2 Abstract

In snow-fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in

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relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km2_{forested catchment in southern} Québec, Canada (47°N, 71°W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high-resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index (LAI), tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small-scale variability across the catchment and 1810 manual snow samples were collected through- out the consecutive winters of 2016–17 and 2017–18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the LAI. We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression, binary regression tree and neural networks (NN). We were unable to establish any relationship between the CV of SWE and the LAI. However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (Nash- Sutcliffe efficiency (NSE) = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE. Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66).

1.3 Introduction

At its maximum, snow can cover up to approximately 40% of continental land surfaces within the Northern Hemisphere (Langlois et al., 2009). Several previous studies have focused on identifying the key variables that explain the spatial variability of seasonal snow distribution. These variables include the climate regime (Winkler and Moore, 2005; Winkler et al., 2005), the elevation (Winkler and Moore, 2005; López-Moreno and Stähli, 2008; Dixon et al., 2014; Roth and Nolin, 2017), the slope aspect (Murray and Buttle, 2003; Jost et al., 2007; Musselman et al., 2008), the solar radiation (Molotch et al., 2005; Musselman et al., 2008; Roth and Nolin, 2017; Mazzotti et al., 2019), and the wind speed (Woods et al., 2006; Broxton et al., 2015) among others.

Obviously, in forest environments, the presence of a canopy, acting as a semi-permeable barrier to precipitation and incoming radiation, has several profound implications on the snow distribution as well. For instance, beneath the canopy, we typically observe reduced snow accumulation and snow water equivalent (SWE) (Plamondon et

al., 1984; Hardy and Hansen-Bristow, 1990) due to snowfall interception, as well as lower snowmelt rates due

to solar shading (Hedstrom and Pomeroy 1998; Molotch et al., 2007 ; Gustafson et al., 2010). Indeed, Musselman et al. (2008) reported snow depth to be 98% higher in forest clearings than in areas with a closed canopy. Winkler et al. (2005) reported 32% and 14% less SWE, respectively, for a mature forest and juvenile

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forest, as compared to a forest clearing. In his study, Pomeroy et al. (2002b) found that snow interception could explain up to 61% of the variability in snow ablation. Depending on local climatic conditions and differences in the vegetation structure, intercepted snow may persist from several days to months (Pomeroy and Schmidt, 1993), in part due to variable sublimation and unloading rates (Moeser et al., 2016), hence contributing to the spatial variability of snow on the ground.

It appears that broad-scale mean vegetation characteristics such as canopy cover or plot-scale mean leaf area index (LAI) should impact snow accumulation and melting. Among the studies that could identify such a relationship, one finds Winkler and Moore (2005) who measured SWE from two locations in coniferous forest of western Canada (nine different stands) on or close to April 1 in 1995, 1996, and 1997. The authors reported that the combination of crown closure and the temporal difference explained 31% to 45% of SWE variability for most of the studied stands. Prévost et al. (1989) and Talbot and Plamondon (2002) explored the relationship between forest and SWE variability in balsam fir stands and concluded that contrasting forest stand characteristics could explain nearly 70% of the stand-average SWE. One should note however that some studies were not successful at connecting forest stand characteristics with SWE variability. For instance, Pomeroy et al. (2002a, 2002b) could not find a clear relationship between the spatial variability of SWE and the LAI. Murray and Buttle (2003) were also unsuccessful at relating the spatial variability of SWE with the canopy gap fraction.

Nowadays, we observe an emergence of techniques such as LiDAR, that allow us to not only to describe the architecture of the forest canopy in great detail (spatial scale ~ 1 m, see Deems et al., 2013; Mazzotti et al., 2019), but also the spatial distribution of snow depth and SWE (Broxton et al., 2019; Mazzotti et al., 2019). Unfortunately, we know relatively little about the effect of very fine-scale vegetation characteristics (distance to nearby tree, microscale variations of LAI, etc.) on the snow distribution. The effect of that fine-scale variability has been mostly overlooked either due to logistical constraints or because it can appear random when measurement errors are taken into account (Jost et al., 2007). One of the few studies to have successfully looked at this issue is Musselman et al. (2008), who explored the relationship between canopy radius and the snow depth distribution. They found 64% more snow depth in an adjacent open area compared to beneath the canopy (for canopy radii less than 2 m). They also reported difference of nearly twice as much between the adjacent open area and beneath the canopy for canopy radii greater than 3 m.

Given the complexity of small-scale interactions between snow and vegetation, the use of statistical approaches is necessary. In the past, numerous studies have used linear regression models (Elder et al., 1991; Talbot et al., 2005; Jost et al., 2007; Meløysund et al., 2007; López-Moreno et al., 2013) and binary regression trees (Erxleben

et al., 2002; Anderton et al., 2004; Molotch et al., 2005; Musselman et al., 2008; Meromy et al., 2013) as SWE

Exploring snowpack properties and processes through observation and modelling : case study of the humid boreal forest in eastern Canada

Exploring snowpack properties and processes through

observation and modelling: A case study of the humid

boreal forest in eastern Canada

Thèse

Achut Parajuli

Doctorat en génie des eaux

Philosophiæ doctor (Ph. D.)

Exploring snowpack properties and processes through

observation and modelling: A case study of the humid boreal

forest in eastern Canada

Thèse

Achut Parajuli

Sous la direction de:

Daniel Nadeau, Directeur de recherche

François Anctil, Codirecteur de recherche

Résumé

Abstract

Contents

List of Figures

List of Tables

Abbreviation

Remerciements

Avant-propos

Introduction

Spatiotemporal variability in snow distribution

Types of snow models

Empirical models

Temperature-index models

Energy balance models

Research gaps

Research objectives

Methods

Study Area

Manual snow sampling design

Snow profiling stations

Supporting dataset

Chapter 1 Exploring the spatiotemporal variability of

the snow water equivalent in a small boreal forest

catchment through observation and modelling

1.1 Résumé

1.2 Abstract

1.3 Introduction