Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas. / Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.

Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas. / Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.

<p>The boreal forests represent 8 % of all forested areas on the earth and have a significant role in the control of greenhouse gases and an impact on global climate change. The main objective of this thesis is to increase the understanding of how evaporation and heat-flux processes in the boreal forest zone are affecting the regional and global climate.</p><p>A meteorological mesoscale model with an advanced land-surface parameterization has been utilized to study aggregation of fluxes of water vapour and heat. The model has been compared against four other methods for flux estimation in a southern boreal landscape. The results show that the mesoscale model is successfully reproducing 24-hour averages of fractionally weighted mast measurements of sensible and latent heat flux.</p><p>The model was also evaluated against in-situ observations of surface fluxes and other meteorological variables. The results reveal that a correct initialization of soil moisture is crucial to simulate a realistic partitioning of the sensible and latent heat fluxes. Significant differences in surface fluxes and friction velocities between two apparently similar forest sites indicate the need for careful assessment of areal representativity when comparing mesoscale model results with in-situ observations.</p><p>A parameterization for the absorption of solar radiation of high-latitude sparse forests was implemented and tested in the model that significantly improved the simulation of high wintertime midday sensible heat fluxes. A scheme for heat storage in vegetation was also implemented which improved the results, but the scheme needs further evaluation for high latitude forests.</p><p>Two commonly used strategies for the description of land-surface heterogeneity, the effective parameter approach and the mosaic approach, were tested in the mesoscale model against airborne observations of sensible and latent heat fluxes. The results show that the mosaic approach produces better results especially when small lakes are present in model grid-squares.</p> / <p>Norra halvklotets barrskogsbälte representerar 8 % av all skogsbeklädd mark på jorden och har stor betydelse för kontrollen av växthusgaser och påverkan på globala klimatförändringar. Syftet med denna avhandling är att öka förståelsen av hur avdunstning och värmeflöden i den boreala skogszonen påverkar klimatet regionalt och globalt.</p><p>En meteorologisk mesoskalemodell med en avancerad landyteparameterisering har använts för att studera aggregering av avdunstning och värmeflöden. Modellen jämfördes med fyra andra metoder för uppskattning av värmeflöden i den boreala skogszonens södra delar. Resultaten visade att mesoskalemodellen reproducerar 24-timmarsmedelvärden av sensibelt och latent värmeflöde från areellt viktade mastmätningar med bra resultat.</p><p>Modellen utvärderades även mot markbaserade mätningar av sensibelt och latent värme och andra meteorologiska variabler. Resultaten visar att en korrekt initialisering av markvatteninnehållet är avgörande för att simulera en realistisk uppdelning av de sensibla och latenta värmeflödena. Markanta skillnader i markyteflöden och friktionshastigheter mellan två liknande skogsmätstationer påvisar nödvändigheten av en noggrann bedömning av den areella representativiteten när man jämför resultat från mesoskalemodellen med markbaserade mätningar.</p><p>En parameterisering för absorption av solstrålning i glesa skogsbestånd på höga breddgrader infördes och testades i modellen vilket markant förbättrade simuleringen av de höga sensibla värmeflöden som observerats vid middagstid på vintern. Ett uttryck för att beskriva värmelagring i vegetationen infördes också vilket förbättrade resultaten, men uttrycket behöver vidare utvärdering för skogsbestånd på höga breddgrader.</p><p>Två ofta använda strategier för att beskriva markytans heterogenitet, effektiva parametermetoden och mosaikmetoden, testades i mesoskalemodellen mot flygburna observationer av sensibla och latenta värmeflöden. Resultaten visar att mosaikmetoden ger bättre resultat särskilt när mindre sjöar förekommer i modellrutorna.</p>

The boreal forests represent 8 % of all forested areas on the earth and have a significant role in the control of greenhouse gases and an impact on global climate change. The main objective of this thesis is to increase the understanding of how evaporation and heat-flux processes in the boreal forest zone are affecting the regional and global climate. A meteorological mesoscale model with an advanced land-surface parameterization has been utilized to study aggregation of fluxes of water vapour and heat. The model has been compared against four other methods for flux estimation in a southern boreal landscape. The results show that the mesoscale model is successfully reproducing 24-hour averages of fractionally weighted mast measurements of sensible and latent heat flux. The model was also evaluated against in-situ observations of surface fluxes and other meteorological variables. The results reveal that a correct initialization of soil moisture is crucial to simulate a realistic partitioning of the sensible and latent heat fluxes. Significant differences in surface fluxes and friction velocities between two apparently similar forest sites indicate the need for careful assessment of areal representativity when comparing mesoscale model results with in-situ observations. A parameterization for the absorption of solar radiation of high-latitude sparse forests was implemented and tested in the model that significantly improved the simulation of high wintertime midday sensible heat fluxes. A scheme for heat storage in vegetation was also implemented which improved the results, but the scheme needs further evaluation for high latitude forests. Two commonly used strategies for the description of land-surface heterogeneity, the effective parameter approach and the mosaic approach, were tested in the mesoscale model against airborne observations of sensible and latent heat fluxes. The results show that the mosaic approach produces better results especially when small lakes are present in model grid-squares. / Norra halvklotets barrskogsbälte representerar 8 % av all skogsbeklädd mark på jorden och har stor betydelse för kontrollen av växthusgaser och påverkan på globala klimatförändringar. Syftet med denna avhandling är att öka förståelsen av hur avdunstning och värmeflöden i den boreala skogszonen påverkar klimatet regionalt och globalt. En meteorologisk mesoskalemodell med en avancerad landyteparameterisering har använts för att studera aggregering av avdunstning och värmeflöden. Modellen jämfördes med fyra andra metoder för uppskattning av värmeflöden i den boreala skogszonens södra delar. Resultaten visade att mesoskalemodellen reproducerar 24-timmarsmedelvärden av sensibelt och latent värmeflöde från areellt viktade mastmätningar med bra resultat. Modellen utvärderades även mot markbaserade mätningar av sensibelt och latent värme och andra meteorologiska variabler. Resultaten visar att en korrekt initialisering av markvatteninnehållet är avgörande för att simulera en realistisk uppdelning av de sensibla och latenta värmeflödena. Markanta skillnader i markyteflöden och friktionshastigheter mellan två liknande skogsmätstationer påvisar nödvändigheten av en noggrann bedömning av den areella representativiteten när man jämför resultat från mesoskalemodellen med markbaserade mätningar. En parameterisering för absorption av solstrålning i glesa skogsbestånd på höga breddgrader infördes och testades i modellen vilket markant förbättrade simuleringen av de höga sensibla värmeflöden som observerats vid middagstid på vintern. Ett uttryck för att beskriva värmelagring i vegetationen infördes också vilket förbättrade resultaten, men uttrycket behöver vidare utvärdering för skogsbestånd på höga breddgrader. Två ofta använda strategier för att beskriva markytans heterogenitet, effektiva parametermetoden och mosaikmetoden, testades i mesoskalemodellen mot flygburna observationer av sensibla och latenta värmeflöden. Resultaten visar att mosaikmetoden ger bättre resultat särskilt när mindre sjöar förekommer i modellrutorna.

This thesis details the development and calibration of a model created by coupling a land surface simulation model named CLASS with a hydrologic model named WATFLOOD. The resulting model, known as WatCLASS, is able to serve as a lower boundary for an atmospheric model. In addition, WatCLASS can act independently of an atmospheric model to simulate fluxes of energy and moisture from the land surface including streamflow. These flux outputs are generated based on conservation equations for both heat and moisture ensuring result continuity. WatCLASS has been tested over both the data rich BOREAS domains at fine scales and the large but data poor domain of the Mackenzie River at coarse scale. The results, while encouraging, point to errors in the model physics related primarily to soil moisture transport in partially frozen soils and permafrost. Now that a fully coupled model has been developed, there is a need for continued research by refining model processes and test WatCLASS's robustness using new datasets that are beginning to emerge. Hydrologic models provide a mechanism for the improvement of atmospheric simulation though two important mechanisms. First, atmospheric inputs to the land surface, such as rainfall and temperature, are transformed by vegetation and soil systems into outputs of energy and mass. One of these mass outputs, which have been routinely measured with a high degree of accuracy, is streamflow. Through the use of hydrologic simulations, inputs from atmospheric models may be transformed to streamflow to assess reliability of precipitation and temperature. In this situation, hydrologic models act in an analogous way to a large rain gauge whose surface area is that of a watershed. WatCLASS has been shown to be able to fulfill this task by simulating streamflow from atmospheric forcing data over multi-year simulation periods and the large domains necessary to allow integration with limited area atmospheric models. A second, more important, role exists for hydrologic models within atmospheric simulations. The earth's surface acts as a boundary condition for the atmosphere. Besides the output of streamflow, which is not often considered in atmospheric modeling, the earth's surface also outputs fluxes of energy in the form of evaporation, known as latent heat and near surface heating, known as sensible heat. By simulating streamflow and hence soil moisture over the land surface, hydrologic models, when properly enabled with both energy and water balance capabilities, can influence the apportioning of the relative quantities of latent and sensible heat flux that are required by atmospheric models. WatCLASS has shown that by improving streamflow simulations, evaporation amounts are reduced by approximately 70% (1271mm to 740mm) during a three year simulation period in the BOREAS northern old black spruce site (NSA-OBS) as compared to the use of CLASS alone. To create a model that can act both as a lower boundary for the atmosphere and a hydrologic model, two choices are available. This model can be constructed from scratch with all the caveats and problems associated with proving a new model and having it accepted by the atmospheric community. An alternate mechanism, more likely to be successfully implemented, was chosen for the development of WatCLASS. Here, two proven and well tested models, WATFLOOD and CLASS, were coupled in a phased integration strategy that allowed development to proceed on model components independently. The ultimate goal of this implementation strategy, a fully coupled atmospheric - land surface - hydrologic model, was developed for MC2-CLASS-WATFLOOD. Initial testing of this model, over the Saguenay region of Quebec, has yet to show that adding WATFLOOD to CLASS produces significant impacts on atmospheric simulation. It is suspected, that this is due to the short term nature of the weather simulation that is dominated by initial conditions imposed on the atmospheric model during the data assimilation cycle. To model the hydrologic system, using the domain of an atmospheric model, requires that methods be developed to characterize land surface forms that influence hydrologic response. Methods, such as GRU (Grouped Response Unit) developed for WATFLOOD, need to be extended to taken advantage of alternate data forms, such as soil and topography, in a way that allows parameters to be selected <I>a priori</I>. Use of GIS (Geographical Information System) and large data bases to assist in development of these relationships has been started here. Some success in creating DEMs, (Digital Elevation Model) which are able to reproduce watershed areas, was achieved. These methods build on existing software implementations to include lake boundaries information as a topographic data source. Other data needs of hydrologic models will build on relationships between land cover, soil, and topography to assist in establishing grouping of these variables required to determine hydrologic similarity. This final aspect of the research is currently in its infancy but provides a platform from which to explore for future initiatives. Original contributions of this thesis are centered on the addition of a lateral flow generation mechanism within a land surface scheme. This addition has shown a positive impact on flux returns to the atmosphere when compared to measured values and also provide increased realism to the model since measured streamflow is reproduced. These contributions have been encapsulated into a computer model known as WatCLASS, which together with the implementation plan, as presented, should lead to future atmospheric simulation improvements.

This thesis details the development and calibration of a model created by coupling a land surface simulation model named CLASS with a hydrologic model named WATFLOOD. The resulting model, known as WatCLASS, is able to serve as a lower boundary for an atmospheric model. In addition, WatCLASS can act independently of an atmospheric model to simulate fluxes of energy and moisture from the land surface including streamflow. These flux outputs are generated based on conservation equations for both heat and moisture ensuring result continuity. WatCLASS has been tested over both the data rich BOREAS domains at fine scales and the large but data poor domain of the Mackenzie River at coarse scale. The results, while encouraging, point to errors in the model physics related primarily to soil moisture transport in partially frozen soils and permafrost. Now that a fully coupled model has been developed, there is a need for continued research by refining model processes and test WatCLASS's robustness using new datasets that are beginning to emerge. Hydrologic models provide a mechanism for the improvement of atmospheric simulation though two important mechanisms. First, atmospheric inputs to the land surface, such as rainfall and temperature, are transformed by vegetation and soil systems into outputs of energy and mass. One of these mass outputs, which have been routinely measured with a high degree of accuracy, is streamflow. Through the use of hydrologic simulations, inputs from atmospheric models may be transformed to streamflow to assess reliability of precipitation and temperature. In this situation, hydrologic models act in an analogous way to a large rain gauge whose surface area is that of a watershed. WatCLASS has been shown to be able to fulfill this task by simulating streamflow from atmospheric forcing data over multi-year simulation periods and the large domains necessary to allow integration with limited area atmospheric models. A second, more important, role exists for hydrologic models within atmospheric simulations. The earth's surface acts as a boundary condition for the atmosphere. Besides the output of streamflow, which is not often considered in atmospheric modeling, the earth's surface also outputs fluxes of energy in the form of evaporation, known as latent heat and near surface heating, known as sensible heat. By simulating streamflow and hence soil moisture over the land surface, hydrologic models, when properly enabled with both energy and water balance capabilities, can influence the apportioning of the relative quantities of latent and sensible heat flux that are required by atmospheric models. WatCLASS has shown that by improving streamflow simulations, evaporation amounts are reduced by approximately 70% (1271mm to 740mm) during a three year simulation period in the BOREAS northern old black spruce site (NSA-OBS) as compared to the use of CLASS alone. To create a model that can act both as a lower boundary for the atmosphere and a hydrologic model, two choices are available. This model can be constructed from scratch with all the caveats and problems associated with proving a new model and having it accepted by the atmospheric community. An alternate mechanism, more likely to be successfully implemented, was chosen for the development of WatCLASS. Here, two proven and well tested models, WATFLOOD and CLASS, were coupled in a phased integration strategy that allowed development to proceed on model components independently. The ultimate goal of this implementation strategy, a fully coupled atmospheric - land surface - hydrologic model, was developed for MC2-CLASS-WATFLOOD. Initial testing of this model, over the Saguenay region of Quebec, has yet to show that adding WATFLOOD to CLASS produces significant impacts on atmospheric simulation. It is suspected, that this is due to the short term nature of the weather simulation that is dominated by initial conditions imposed on the atmospheric model during the data assimilation cycle. To model the hydrologic system, using the domain of an atmospheric model, requires that methods be developed to characterize land surface forms that influence hydrologic response. Methods, such as GRU (Grouped Response Unit) developed for WATFLOOD, need to be extended to taken advantage of alternate data forms, such as soil and topography, in a way that allows parameters to be selected <I>a priori</I>. Use of GIS (Geographical Information System) and large data bases to assist in development of these relationships has been started here. Some success in creating DEMs, (Digital Elevation Model) which are able to reproduce watershed areas, was achieved. These methods build on existing software implementations to include lake boundaries information as a topographic data source. Other data needs of hydrologic models will build on relationships between land cover, soil, and topography to assist in establishing grouping of these variables required to determine hydrologic similarity. This final aspect of the research is currently in its infancy but provides a platform from which to explore for future initiatives. Original contributions of this thesis are centered on the addition of a lateral flow generation mechanism within a land surface scheme. This addition has shown a positive impact on flux returns to the atmosphere when compared to measured values and also provide increased realism to the model since measured streamflow is reproduced. These contributions have been encapsulated into a computer model known as WatCLASS, which together with the implementation plan, as presented, should lead to future atmospheric simulation improvements.

Die vorliegende Arbeit hat zum Ziel, den Einfluss der Rückkopplung zwischen Landoberfläche und Atmosphäre auf den regionalen Wasserhaushalt abzuschätzen. Dazu erfolgen Modellsimulationen mit dem gekoppelten Vegetations-Grenzschichtmodell HIRVAC (HIgh Resolution Vegetation Atmosphere Coupler) für das Einzugsgebiet Sperrgraben (Bayerische Alpen). Im Ergebnis wird der Zusammenhang zwischen dem Entkopplungsfaktor Omega und der Verdunstung als Wasserhaushaltsgröße für einen festgelegten Zeitraum untersucht. Die Kombination eines vertikal hochaufgelösten Grenzschichtmodells (HUB) mit einem mechanistischen Photosynthesemodell (PSN6) im Modell HIRVAC ermöglicht eine detaillierte physikalische Beschreibung der turbulenten Austauschprozesse innerhalb der atmosphärischen Grenzschicht. Gleichzeitig werden die Wechselwirkungen zwischen Vegetation und Atmosphäre für jede Modellschicht innerhalb des Bestandes und zu jedem Modellzeitschritt simuliert. Die Definition des Entkopplungsfaktors erfordert die Festlegung eines geeigneten Referenzniveaus über der Vegetationsobergrenze zur Ermittlung der Widerstände gegen den turbulenten Austausch von Wärme und Feuchte. Die Bestimmung dieser Modellschichthöhe wird nach Untersuchungen zur Ausbildung der dynamischen Grenzschicht sowie der Vertikalprofile der Transportwiderstände und des Omega-Faktors vorgenommen. Die dabei erzielten Ergebnisse zum höhenabhängigen Verlauf des Entkopplungsfaktors über der Wiesenfläche und dem Fichtenbestand zeigen, dass mit dem Modell HIRVAC das unterschiedliche Kopplungsverhalten von kleinen Beständen mit glatter Oberfläche (Oberflächenrückkopplung) und hohen, rauen Beständen (Grenzschichtrückkopplung) qualitativ und auch quantitativ sehr gut wiedergegeben werden kann. Die Sensitivitätsstudien für die Landnutzungsarten Fichte und Wiese verdeutlichen den Einfluss veränderter Bestandesparameter wie Bestandeshöhe, LAI und Kronenschlussgrad auf den Entkopplungsfaktor und die Evapotranspiration. Sehr gut ersichtlich wird außerdem das unterschiedliche atmosphärische Turbulenzspektrum durch die Verwendung verschiedener Schließungsansätze im Modell und deren Einfluss auf die turbulenten Diffussionskoeffizienten. Die Ergebnisse werden mit dem Ziel der Ableitung einfacher Zusammenhänge zu Landschaftskennziffern parametrisiert. Zur Bereitstellung von flächenhaften Klimadaten wird das Modell HIRVAC mit einem Geographischen Informationssystem (ArcView) gekoppelt. Das Modell HIRGIS bietet eine geeignete Basis für die Regionalisierung von Klimagrößen im kleinräumig strukturierten Gelände. Auf der Grundlage der digitalen Gelände- und Landnutzungsdaten können topoklimatisch beeinflusste Größen, wie z.B. Einstrahlung, Temperatur, Strahlungsbilanz und Verdunstung für Gebiete flächendeckend berechnet werden. In den Ergebniswerten sind die Rückkopplungseffekte zwischen Bestand und Atmosphäre in aktueller Form enthalten. Außerdem entfallen Generalisierungseffekte, wie sie bei statistischen Übertragungsmethoden (Interpolation von Messwerten) auftreten. Durch die Möglichkeit der messwertunabhängigen Modellierung kann HIRGIS prinzipiell für Regionen mit anderem Gebietscharakter eingesetzt werden. Bei der Anwendung von HIRGIS auf das Einzugegebiet Sperrgraben wird allerdings deutlich, wie wichtig eine präzise Anpassung der Modellparametrisierung, insbesondere der Vegetation, an den Standort ist. Die erzeugten Karten zu den Klimagrößen liefern dem Nutzer eine gute Grundlage für klimatologische Gebietsinformationen. Eine Quantifizierung des Einflusses der Rückkopplung auf die Verdunstung im Einzugsgebiet Sperrgraben erweist sich für den untersuchten Zeitraum als schwierig. Die Ergebnisse zur Verdunstung zeigen trotzdem eine sehr gute Übereinstimmung mit Transpirationswerten aus Saftflussmessungen für Buche und Fichte im Einzugsgebiet Sperrgraben und decken sich mit den Transpirationswerten aus der Literatur für vergleichbare Bestände.

The aim of this thesis is the modeling of surface directional thermal radiation and angular correction on the LST by using empirical and physical methods as well as the analysis of field validation. The work has conducted to some conclusions. The directional emissivity of natural surfaces was obtained from MODIS emissivity product and then used in the split-window algorithm for angular correction on LST. The parameterization models of directional emissivity and thermal radiation were developed. As for the non-isothermal pixels, the daytime-TISI method was proposed to retrieve directional emissivity and effective temperature from multi-angular middle and thermal infrared data. This was validated using an airborne dataset. The kernel-driven BRDF model was checked in the thermal infrared domain and its extension was used to make angular normalization on the LST. A new model, namely FovMod that concerns on the footprint of ground sensor, was developed to simulate directional brightness temperature of row crop canopy. Based on simulation result of the FovMod, an optimal footprintfor field validation of LST was obtained. This thesis has systematically investigated the topic of directional thermal radiation and angular correction on surface temperature and its findings will improve the retrieval accuracy of temperature and emissivity from remotely sensed data and will also provide suggestion for the future design of airborne or spaceborne multi-angular thermal infrared sensors and also for the ground measurement of surface parameters.

L’évolution du climat sur les prochaines dizaines voire centaines d’années pose de nombreuses interrogations, du fait de l’impact de l’homme. Les émissions de gaz à effet de serre depuis le début de l’ère industrielle entrainent une augmentation des températures. Celle-ci est susceptible d’affecter les écosystèmes terrestres, notamment dans les régions boréales où les augmentations de température observées et projetées sont plus importantes. Une évolution de ces écosystèmes peut entrainer des rétroactions sur le climat. Ainsi le phénomène actuel observé de verdissement des régions boréales (ou « Arctic greening ») peut augmenter ce réchauffement via une diminution de l’albédo. Afin de répondre à ces interrogations, des modèles climatiques ont été développés, intégrant des modèles de surface continentale représentant les flux de matière et d’énergie. Le travail effectué dans cette thèse a été mené à partir de l’un d’eux, le modèle de surface continentale ORCHIDEE, qui comprend une description succincte de la végétation boréale. L’objectif de cette thèse était donc l’implémentation puis la modélisation de la végétation boréale.Afin de décrire la végétation présente au niveau des hautes latitudes, i.e. les toundras et les steppes, de nouveaux types de végétation (PFTs) ont été intégrés au modèle à partir des PFTs déjà présents. Tout d’abord, les plantes non vasculaires (NVPs) ont été introduites pour représenter les lichens et les bryophytes, ensuite les buissons pour représenter une strate intermédiaire entre les arbres et les herbacées, et enfin des herbacées C3 boréales pour distinguer la végétation considérée dans les steppes boréales et les prairies tempérées. La description de cette végétation boréale s’est accompagnée de l’intégration de nouveaux processus caractéristiques, allant de l’implémentation d’interactions nouvelles telles que la protection des buissons par la neige en hiver, au simple choix de nouveaux paramètres du PFT, en passant par la modification de processus déjà présents dans le modèle comme la conductance stomatique des NVPs. D’autres processus en lien avec la végétation ont également été mis à jour ou corrigés. Enfin, pour modéliser la dynamique de la végétation boréale, les nouveaux PFTs ont été intégrés à la description initialement présente dans le modèle.Ces modifications ont permis de modéliser la végétation boréale et ses impacts sur les autres variables du système (flux de matière ou d’énergie), soit avec une végétation prescrite (simulations de la période récente), soit avec une végétation dynamique (simulations présentes et futures, à partir des scénarios RCPs 4.5 et 8.5). Les simulations effectuées avec la végétation prescrite montrent que l’on représente mieux le comportement de la végétation avec les nouveaux PFTs. Avec les PFTs originaux la productivité et la biomasse étaient surestimées dans les régions boréales et entrainaient une sous-estimation de l’albédo et une surestimation de la transpiration. Les simulations avec une végétation dynamique ont démontré la capacité du modèle à représenter avec la nouvelle végétation boréale les biomes actuels ainsi que l’« Arctic greening ». Par contre, l’embuissonement observé dans plusieurs études n’a pas été reproduit. Globalement l’introduction des PFTs boréaux s’est traduite par une meilleure description des écosystèmes arctiques et des échanges d’énergie et de matière avec l’atmosphère. Par contre, la protection du pergélisol par les NVPs n’a pas été aussi importante qu’attendu et a été compensée par une augmentation de l’humidité du sol.L’introduction de la nouvelle végétation boréale dans le modèle ORCHIDEE semble donc pertinente et met en évidence l’importance de la représentation de ces écosystèmes. Ce travail ouvre donc des perspectives pour améliorer les simulations climatiques, tant futures que passées. Comme la modélisation de la végétation depuis l’Holocène afin de simuler la quantité de carbone contenu aujourd’hui dans le pergélisol. / Climate evolution over the next ten to hundred years involves many questions, linked to the impact of man. Indeed, greenhouse gases emissions since the beginning of the industrial era lead to an increase in temperature. The latter can affect terrestrial ecosystems, particularly in boreal regions where observed and projected temperature increase is larger than in mid-latitudes. Evolution of these ecosystems can trigger climate feedbacks. For example, the currently observed « Arctic greening » phenomenon could enhance the warming via a decrease in albedo due to the increase in vegetation cover. In order to address these questions, climate models were developped, including continental surface models taking into account the fluxes of mass and energy. In this thesis, such a model was used, the continental surface scheme ORCHIDEE, which includes a succinct description of boreal vegetation. The aim of this work was thus the implementation and the modeling of boreal vegetation.In order to describe high-latitude vegetation, i.e. toundras and steppes, new plant functional types (PFTs) were integrated into the model based on existing PFTs. First, non-vascular plants (NVPs) were integrated to represent lichens and bryophytes found in desert toundras and peatlands, then shrubs to represent an intermediate stratum between trees and grasses in toundras, and finally boreal C3 grasses to distinguish vegetation found in boreal steppes and temperate grasslands. The description of this boreal vegetation was accompanied by the integration of new charachteristic processes, from the implementation of new interactions such as the protection of shrubs by snow in winter, to the simple choice of new PFT parameters such as the lower photosynthetic capacity of boreal C3 grasses compared to temperate C3 grasses, through the modification of existing processes such as the stomatal conductance of NVPs. Other processes linked to vegetation were also updated or corrected. Finally, to model the dynamics of boreal vegetation, new PFTs were integrated into the initial description in the model.Those changes enabled the modeling of boreal vegetation and its impact on other variables (mass or energy fluxes), either using a prescribed vegetation (simulations on the recent period), or using a dynamical vegetation (recent and future simulations using RCPs 4.5 and 8.5). Simulations using the prescribed vegetation indicated that vegetation behaviour is better represented with the new PFTs. With original PFTs, productivity and biomass were overestimated in boreal regions, and lead to an underestimation of albedo and an overestimation of transpiration. Simulations using a dynamical vegetation demonstrated the ability of the model, using the new boreal vegetation, to represent current-day biomes as well as « Arctic greening ». However, the shrubification observed in several studies was not reproduced. Similarly, the impact of new PFTs on other model outputs is important, with for example a decrease in productivity or albedo in winter compared to the original vegetation. Thus, the introduction of boreal PFTs generally resulted in a better description of Arctic ecosystems and of the exchanges of energy and mass with the atmosphere. On the other hand, the protection of permafrost by NVPs was not as substantial as expected and was compensated by an increase in soil humidity (due to shrubs and boreal grasses).The introduction of the new boreal vegetation in the ORCHIDEE model thus seems relevant, and highlights the importance of representing these ecosystems. This work opens up new perspectives to improve future and past climate simulations. The next step consists in modeling vegetation since the Holocene into the future in order to simulate the current amounts of carbone in the permafrost, and to project the outcome of these stocks in the context of climate change and permafrost melt.

The representation of high-latitude surface processes and quantifying surface-climate feedbacks are some of the most serious shortcomings of present day Arctic land surface modelling. The energy balance of seasonally snow-covered sparse deciduous forests at high latitudes is poorly understood and inaccurately represented within hydrological and climate models. Snow cover plays an important role in wintertime fluxes of energy, water and carbon, controlling the length of the active growing season and hence the overall carbon balance of Arctic ecosystems. Snow cover is non-uniform and spatially variable, as wind redistributes snow from areas of exposed open tundra to sheltered areas within the forest, where a deeper snowpack develops. Low solar zenith angles, coupled with sparse deciduous leafless trees, cast shadows across the snow surface. The spatial distribution of canopy gaps determines the timing of direct radiation which penetrates down through the canopy to the snow surface. The forest canopy also excludes incoming longwave radiation and yet also emits longwave radiation to the snow surface. Consequently the forest canopy plays a key role in the radiation balance of sparse forests. To improve our knowledge of these complex processes, meteorological and field observations were taken in an area of highly heterogeneous birch Betula pubescens ssp. czerepanovii forest in Abisko, Sweden during the spring of 2008 and 2009. Detailed measurements of short and longwave radiation above and below the canopy, hemispherical photographs, tree temperatures and snow surveys were conducted to quantify the radiation balance of the sparse deciduous forest. An array of below canopy pyranometers found the mean canopy transmissivity to be 74 % in 2008 and 76 % in 2009. Hemispherical photographs taken at the pyranometer locations analysed with Gap Light Analyzer (GLA) showed reasonable agreement with a mean canopy transmissivity of 75 % in 2008 and 74 % in 2009. The canopy transmissivity was found to be independent of the diffuse fraction of radiation as the canopy is very sparse. A series of survey grids and transects were established to scale up from the below canopy pyranometers to the landscape scale. Hemispherical photographs analysed with GLA showed the sparse forest canopy had a mean transmissivity of 78 % and a mean LAI of 0.25, whereas the open tundra had a mean transmissivity of 97 % and a mean LAI of < 0.01. Snow surveys showed the sparse forest snow depth to vary between 0.34 and 0.55 m, whereas the snow depth in the open tundra varied between 0.12 and 0.18 m. Observations of canopy temperatures showed a strong influence of incident shortwave radiation warming the tree branches to temperatures up to 15 °C warmer than ambient air temperature on the south facing sides of the trees, and up to 6 °C on the north facing sides of the trees. To reproduce the observed radiation balance, two canopy models (Homogenous and Clumped) were developed. The Homogeneous canopy model assumes a single tree tile with a uniform sparse canopy. The Clumped canopy model assumes a tree and a grass tile, where the tree tile is permanently in shade from the canopy and the grass tile receives all the incoming radiation. These canopy models identified the need for a parameter that accounts for the spatial and temporal variation of the shaded gaps within the sparse forest. JULES (Joint UK Land Environment Simulator) is the community land surface model used in the UK Hadley Centre GCM suite. Modifications of the land-surface interactions were included in JULES to represent the shaded gaps within the sparse deciduous forest. New parameterisations were developed for the time-varying sunlit fractions of the gap (flit), the sky-view fraction (fv), and the longwave radiation emitted from the canopy (LWtree). These model developments were informed by field observations of the forest canopy and evaluated against the below canopy short and longwave radiation observed data sets. The JULES Shaded gap model output showed a strong positive relationship with the observations of below canopy shortwave and longwave radiation. The JULES Shaded gap model improves the ratio of observed to modelled short and longwave radiation on sunny days compared to the JULES model. The JULES Shaded gap model reduces the time to snow melt by 2 to 4 days compared to the JULES model, making the model output more aligned with in-situ observational data. This shortening of the modelled snow-season directly impacts on the simulated carbon and water balance regionally and has wider relevance at the pan-Arctic scale. When JULES Shaded Gap was evaluated on the global scale, it improved the modelled snowmass across large areas of sparse forest in northern Canada, Scandinavia and Northern Russia with respect to GlobSnow. The performance of the land surface-snow-vegetation interactions of JULES was improved by using the Shaded gap to model the radiation balance of sparse forests in climate-sensitive Arctic regions. Furthermore these observational data can be used to develop and evaluate high latitude land-surface processes and biogeochemical feedbacks in other earth system models.