Ces travaux ont pour objectif général d'améliorer la représentation spatio-temporelle des processus hydrologiques de surface à partir de modèles dont la complexité est adaptée aux informations disponibles par la télédétection multi-capteur/multi-résolution. Nous avons poursuivi des développements méthodologiques (désagrégation, assimilation, modélisation du bilan d'énergie) autour de l'estimation de l'humidité du sol dans le contexte de la gestion des ressources en eau dans les régions semi-arides. Récemment, des missions spatiales permettent d'observer l'humidité des sols en surface; notamment avec le capteur AMSR-E (Advanced Microwave Scanning Radiometer-EOS) et la mission SMOS (Soil Moisture Ocean Salinity). Toutefois la résolution spatiale de ces capteurs est trop large (> 40 km) pour des applications hydrologiques. Afin de résoudre le problème d'échelle, l'algorithme de désagrégation DisPATCh (Disaggregation based on Physical and Theoretical Scale Change) a été développé en se basant sur un modèle d'évapotranspiration. Dans la première partie de thèse, l'algorithme est appliqué et validé sur le bassin du Murrumbidgee (sud-est de l'Australie) avec une résolution spatiale cible de 1 km à partir des données de LST (Température de surface) et NDVI (indice de végétation) issues de MODIS (MODerate resolution Imaging Spectroradiometer) et de deux produits d'humidité du sol basse résolution : SMOS et AMSR-E. Les résultats montrent que la désagrégation est plus efficace en été, où la performance du modèle d'évapotranspiration est optimale. L'étude précédente a notamment mis en évidence que la résolution temporelle des données DisPATCh est limitée par la couverture nuageuse visible sur les images MODIS et la résolution temporelle des radiomètres micro-ondes (3 jours pour SMOS). Dans la deuxième partie, une nouvelle approche est donc développée pour assurer la continuité temporelle des données d'humidité de surface en assimilant les données DisPATCh dans un modèle dynamique de type force-restore, forcé par des données météorologiques issus de ré-analyses, dont les précipitations. La méthode combine de manière originale un système variationnel (2D-VAR) pour estimer l'humidité du sol en zone racinaire et une approche séquentielle (filtre de Kalman simplifié) pour analyser l'humidité du sol en surface. La performance de l'approche est évaluée sur deux zones: la région Tensift-Haouz au Maroc et la région de Yanco en Australie. Les résultats montrent que le couplage désagrégation/assimilation de l'humidité du sol est un outil performant pour estimer l'humidité en surface à l'échelle journalière, même lorsque les données météorologiques sont incertaines. Dans la troisième partie, une méthode de correction des effets topographiques sur la LST est développée dans le but d'étendre l'applicabilité de DisPATCh aux zones vallonnées ou montagneuses qui jouent souvent le rôle de château d'eau sur les régions semi-arides. Cette approche, basée sur un modèle de bilan d'énergie à base physique, est testée avec les données ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) et Landsat sur la vallée d'Imlil dans le Haut Atlas Marocain. Les résultats indiquent que les effets topographiques ont été fortement réduits sur les images de LST à ~100 m de résolution et que la LST corrigée pourrait être utilisée comme une signature de l'état hydrique en montagne. Les perspectives ouvertes par ces travaux concernent la correction/désagrégation des données de précipitations et l'estimation des apports par l'irrigation pour une gestion optimisée de l'eau. / This thesis aims to improve the spatio-temporal resolution of surface water fluxes at the land surface-atmosphere interface based on appropriate models that rely on readily available multi-sensor remote sensing data. This work has been set up to further develop (disaggregation, assimilation, energy balance modeling) approaches related to soil moisture monitoring in order to optimize water management over semi-arid areas. Currently, the near surface soil moisture data sets available at global scale have a spatial resolution that is too coarse for hydrological applications. Especially, the near surface soil moisture retrieved from passive microwave observations such as AMSR-E (Advanced Microwave Scanning Radiometer-EOS) and SMOS (Soil Moisture and Ocean Salinity) data have a spatial resolution of about 60 km and 40 km, respectively. In this context, the downscaling algorithm "DISaggregation based on Physical And Theoretical scale Change" (or DisPATCh) has been developed. The near surface soil moisture variability is estimate within a low resolution pixel at the targeted 1 km resolution based on an evapotranspiration model using LST (Land surface temperature) and NDVI (vegetation index) derived from MODIS (MODerate resolution Imaging Spectroradiometer) data. Within a first step, DisPATCh is applied to SMOS and AMSR-E soil moisture products over the Murrumbidgee river catchment in Southeastern Australia and is evaluated during a one-year period. It is found that the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. However, the temporal resolution of DisPATCh data is limited by the gaps in MODIS images due to cloud cover, and by the temporal resolution of passive microwave observations (global coverage every 3 days for SMOS). The second step proposes an approach to overcome these limitations by assimilating the 1 km resolution DisPATCh data into a simple dynamic soil model forced by reanalysis meteorological data including precipitation. The original approach combines a variational scheme for root-zone soil moisture analysis and a sequential approach for the update of surface soil moisture. The performance is assessed using ground measurements of soil moisture in the Tensift-Haouz region in Morocco and the Yanco area in Australia during 2014. It is found that the downscaling/assimilation scheme is an efficient approach to estimate the dynamics of the 1 km resolution surface soil moisture at daily time scale, even when coarse scale and inaccurate meteorological data including rainfall are used. The third step presents a physically-based method to correct LST data for topographic effects in order to offer the opportunity for applying DisPATCh over mountainous areas. The approach is tested using ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) and Landsat data over a 6 km by 6 km steep-sided area in the Moroccan Atlas. It is found that the strong correlations between LST and illumination over rugged terrain before correction are greatly reduced at ~100 m resolution after the topographic correction. Such a correction method could potentially be used as a proxy of the surface water status over mountainous terrain. This thesis opens the path for developing new remote sensing-based methods in order to retrieve water inputs -including both precipitation and irrigation- at high spatial resolution for water management.

No description available.

The increasing urbanization of the greater Salt Lake City area (GSLA) has contributed to the development of an urban canopy over this area. This canopy refers to the effects of building profiles, varying land surface properties and anthropogenic heating on local meteorological conditions including temperature, humidity, and wind velocity. Urban Canopy Models (UCMs) can be used to represent these characteristics on a mesoscale without needing to develop models accounting for effects of individual buildings. One method used to classify urban areas are Local Climate Zones (LCZs), which assign different properties to different types of urban areas. A baseline model that represents current GSLA conditions was developed using a series of sensitivity studies, which focused on the effects of mesh resolution, land surface models, UCMs, anthropogenic heating rates and LCZ urban classifications. The baseline model was validated using measured meteorological data. Four urban growth scenarios were compared to this baseline model to evaluate the effects of future growth on local 2-meter air temperatures, 2-meter relative humidity, and 10-meter wind speed. Results showed increased urban density did not affect daytime temperatures within the GSLA, but did significantly increase local nighttime temperatures. The effects of anthropogenic heating rates were most noticeable during early nighttime hours. Also, increased urbanization affected local temperatures, but did not appear to have "downwind" effects on other areas. A User Guide documenting the modeling approach was developed to support additional studies.

No description available.

This thesis explores the relationship between surface elevation roughness and tornado frequency throughout the eastern United States. It builds upon previous studies which demonstrated a negative relationship between roughness and tornado frequency for the Great Plains and Arkansas. A generalized linear model with tornado frequency as the response variable and roughness and population density as the predictors is generated. This model demonstrates that increased roughness is associated with decreased tornado frequency at the scale of the entire eastern United States, especially where roughness is greater than 20 meters. The methods are also performed for 13 smaller regions within the eastern US, but an effect of roughness is only confirmed for the regions encompassing the Great Lakes and central Appalachians. From these results, it is concluded that mountain ranges, where roughness exceeds 20 meters, clearly inhibit tornado activity, but there is little evidence that smaller terrain variations have the same effect.

A mesoscale evaluation is performed to determine the impacts of climate change on terrestrial hydrological components and the Net Irrigation Water Requirement (NIWR) throughout the Chesapeake Bay watershed in the mid-Atlantic region of the United States. The Noah-MP land surface model is calibrated and evaluated against the observed datasets of United States Geological Survey (USGS) streamflow gages, actual evapotranspiration from USGS Simplified Surface Energy Balance (SSEBop) Model and soil moisture from Soil Analysis Climate Network (SCAN). Six best performing Global Climate Models (GCM) based on Multivariate Adaptive Constructed Analogs (MACA) scheme are included for two future scenarios (RCP 4.5 and RCP 8.5), to assess the change in water balance components, change in NIWR for two dominant crops (corn and soybeans) and uncertainty in GCM projections. Using these long-term simulations, the flood inundation maps are developed for future scenarios along the Susquehanna River including the City of Harrisburg in Pennsylvania. The HEC-RAS 2D model is calibrated and evaluated against the high-water marks from major historical flood events and the stage-discharge relationship of the available USGS streamgages. Finally, the impacts of climate change are assessed on flood inundation depth and extent by comparing a 30-yr and 100-yr flood event based on the historical and future (scenario-based) peak discharge estimates at the USGS streamgages. Interestingly, flood inundation extent and severity predicted by the model along the Susquehanna River near Harrisburg is expected to rise in the future climate scenarios due to the greater frequency of extreme events increasing total precipitation. / Master of Science / Climate change is inevitable due to increased greenhouse gas emissions, with impacts varying in space and time significantly throughout the globe. The impacts are strongly driven by the change in precipitation and temperature which affect the control of the movement of water on the surface of the Earth. These changes in the water cycle require an understanding of hydrological components like streamflow, soil moisture, and evapotranspiration. Development of long-term climate models and computational hydrological models (based on mathematical equations and governed by laws of physics) has helped us in understanding this climate variability in space and time. This study performs a long-term simulation using the datasets from six different climate models to analyze the change in terrestrial hydrological components for the entire Chesapeake Bay watershed in the mid-Atlantic region of the United States. The simulations provide an understanding of the interplay between various land surface processes due to climate change and can help determine future water availability and consumption. To illustrate the usefulness of such long-term simulations, the crop water requirement is quantified for the dominant crops in Chesapeake Bay watershed to project water availability and support the development of mitigation strategies. Flood inundation maps are also developed for a section of Susquehanna River near the City of Harrisburg in south-central Pennsylvania using the streamflow from long-term simulations. The flood inundation depth and extent for major flood events such as Tropical Storm Agnes (1972) and Tropical Storm Lee (2011) are compared along the Susquehanna River, which can aid in managing flood operations, reduce the future flood damages and prioritize the mitigation efforts for endangered communities near the City of Harrisburg.

In dieser Studie wird ein neues Modul “CLM-Palm” für mehrjährige Nutzpflanzen zur Modellierung einer funktionellen Gruppe (plant functional type) für Ölpalmen im Rahmen des Community Land Models (CLM4.5) entwickelt, um die Auswirkungen der Transformation eines tropischen Waldes in eine Ölpalmenplantage auf die Kohlenstoff-, Wasser- und Energieflüsse zwischen Land und Atmosphäre zu quantifizieren. Um die Morphologie der Ölpalme möglichst detailgetreu darzustellen (das heißt, dass ungefähr 40 Phytomere einen mehrschichtigen Kronenraum formen), wird in dem Modul CLM-Palm eine phänologische und  physiologische Parametrisierung auf Skalen unterhalb des Kronraums eingeführt, so dass jedem Phytomer sein eigenes prognostisches Blattwachstum und seine Erntekapazität zugeordnet wird, während Stamm und Wurzeln gemeinsam genutzt werden. Das Modul CLM-Palm wurde ausschließlich für Ölpalmen getestet, ist aber auch für andere Palmarten (z. B. Kokospalmen) interessant.  Im ersten Kapitel dieser Arbeit werden Hintergrund und Motivation dieser Arbeit vorgestellt. In Kapitel 2 wird die Entwicklung des Haupt- bzw. Kernmodells beschrieben,  inklusive Phänologie und Allokationsfunktionen zur Simulation des Wachstums und des Ertrags der Palme PFT, wodurch die Basis zur Modellierung  der biophysikalischen und biogeochemicalischen Kreisläufe innerhalb dieser Monokultur bereitgestellt wird. Die neuen Parameter für die Phänologie und die Allokation wurden sorgfältig mit Feldmessungen des Blattflächenindexes (LAI), des Ertrags und der Nettoprimärproduktion (NPP) verschiedener Ölpalmenplantagen auf Sumatra (Indonesien) kalibriert und validiert. Die Validierung zeigte die Eignung von CLM-Palm zur adäquaten Vorhersage des mittleren Blattwachstums und Ertrags für verschiedene Standorte und repräsentiert in ausreichendem Maß die signifikante Variabilität bezüglich des Stickstoffs und Alters von Standort zu Standort.  In Kapitel 3 wird die weitere Modellentwicklung und die Implementierung eines Norman-Mehrschichtmodells für den Strahlungstransport vorgestellt, das an den  mehrschichtigen Kronenraum der Ölpalme angepasst ist. Dieses Norman-Mehrschichtmodell des Strahlungstransports zeigte im Vergleich zu dem in CLM4.5 implementierten Standardmodell (basierend auf großen Blättern) bei der Simulation der Licht-Photosynthese-Kurve leichte Verbesserungen und hat  lediglich marginale Vorteile gegenüber dem ebenfalls in CLM4.5 implementierten alternativen statistischen Mehrschichtmodell.  Dennoch liefert das Norman-Modell eine detailliertere und realistischere Repräsentation des Belaubungszustands wie etwa dem dynamischen LAI, der Blattwinkelverteilung in verschiedenen Höhen, und ein ausgewogeneres Profil der absorbierten photosynthetisch aktiven Strahlung (PAR). Die Validierung mit Hilfe der Eddy-Kovarianz Flussdaten zeigte die Stärke von CLM-Palm bei der Simulation der Kohlenstoffflüsse, offenbarte aber auch Abweichungen in der simulierten Evapotranspiration (ET), dem sensiblen und dem latenten Wärmefluss (H und LE). Eine Reihe von hydrologischen Messungen im Kronenraum wird in Kapitel 4 beschrieben. Dies beinhaltet eine Adaption des in CLM4.5 eingebauten Standardmodells für Niederschlag, Interzeption und Speicherfunktionen für die speziellen Merkmale eines Ölpalmen-Kronenraums. Die überarbeitete Hydrologie des Kronenraums behob die Probleme bei der Simulation der Wasserflüsse (ET und Transpiration im Kronenraum) und verbesserte die Energieaufteilung zwischen H und LE. Kapitel 5 dokumentiert die Implementierung eines neuen dynamischen Modells für Stickstoff (nitrogen, N) in CLM-Palm zur Verbesserung der Simulation der C- und N-Dynamik, insbesondere mit Bezug auf den N-Düngeeffekte in landwirtschaftlich genutzten Systemen. Das dynamische N-Modell durchbricht die Limitierung des Standardmodells in CLM4.5, mit fixierter C-N-Stöchiometrie und erlaubt die Variation des C:N-Verhältnisses in lebendem Gewebe in Abhängigkeit der N-Verfügbarkeit und dem N-Bedarf der Pflanze.  Eine Reihe von Tests bezüglich der Düngung zeigte beispielhaft die Vorteile des dynamischen N-Modells, wie zum Beispiel die Verbesserung des Netto-Ökosystemaustauschs (net ecosystem exchange, NEE), ein realistischeres C:N-Verhältnis im Blatt, eine verbesserte Repräsentation der Effizienz des Stickstoffeinsatzes (nitrogen-use efficiency, NUE), sowie der Effekte von Düngung auf Wachstum und Ertrag. Abschließend wird in Kapitel 6 eine Anwendungsstudie gezeigt, in der die zentralen Modellentwicklungen aus den vorangegangenen Kapiteln verwendet werden. Eine junge und eine  erntereife Ölpalmenplantage sowie ein Primärregenwald wurden simuliert und verglichen. Sie wiesen klare Unterschiede in den C-Flüssen und in den biophysikalischen Merkmalen (z.B. ET und Oberflächentemperatur) auf. Ölpalmenplantagen können durch Wachstumsentwicklung (im Alter von etwa 4 Jahren)  ebenso hohe und darüber hinausgehende C-Assimilation und Wassernutzungsraten erreichen wie Regenwälder, haben jedoch im Allgemeinen eine höhere Oberflächentemperatur als eine bewaldete Fläche – dies gilt auch für erntereife Plantagen. Eine Simulation des Übergangs, die zwei Rotationsperioden mit Neubepflanzungen alle 25 Jahre umspannt, zeigte dass der Anbau von Ölpalmen auf längeren Zeitskalen lediglich in etwa die Hälfte des ursprünglichen C-Speichers der bewaldeten Fläche vor dem Kahlschlag  rückspeichern kann. Das im Boden gespeicherte C nimmt in einer bewirtschafteten Plantage aufgrund des begrenzten Streurücklaufs langsam und graduell ab. Insgesamt reduziert die Umwandlung eines Regenwaldes in eine Ölpalmenplantage die langfristigen C-Speicher und die Kapazität der Fläche zur C-Sequestrierung und trägt potentiell zur Erwärmung der Landoberfläche bei – trotz des schnellen Wachstums und der hohen C-Assimilationsrate einer stark gedüngten Plantage. Zur Einschätzung der regionalen und globalen Effekte der Ausbreitung der Kultivierung von Ölpalmen auf die Austauschprozesse zwischen Land und Atmosphäre und auf das Klima ist es notwendig eine Upscaling-Studie durchzuführen.

Uncertainty of predicted change in precipitation frequency and intensity motivates the scientific community to better understand, quantify, and model the possible outcome of dryland ecosystems. In pulse dependent ecosystems (i.e. monsoon driven) soil moisture is tightly linked to atmospheric processes. Here, I analyze three overarching questions; Q1) How does soil moisture presence or absence in a shallow or deep layer influence the surface energy budget and planetary boundary layer characteristics?, Q2) What is the role of vegetation on ecosystem albedo in the presence or absence of deep soil moisture?, Q3) Can we develop empirical relationships between soil moisture and the planetary boundary layer height to help evaluate the role of future precipitation changes in land surface atmosphere interactions?. To address these questions I use a conceptual framework based on the presence or absence of soil moisture in a shallow or deep layer. I define these layers by using root profiles and establish soil moisture thresholds for each layer using four years of observations from the Santa Rita Creosote Ameriflux site. Soil moisture drydown curves were used to establish the shallow layer threshold in the shallow layer, while NEE (Net Ecosystem Exchange of carbon dioxide) was used to define the deep soil moisture threshold. Four cases were generated using these thresholds: Case 1, dry shallow layer and dry deep layer; Case 2, wet shallow layer and dry deep layer; Case 3, wet shallow layer and wet deep layer, and Case 4 dry shallow and wet deep layer. Using this framework, I related data from the Ameriflux site SRC (Santa Rita Creosote) from 2008 to 2012 and from atmospheric soundings from the nearby Tucson Airport; conducted field campaigns during 2011 and 2012 to measure albedo from individual bare and canopy patches that were then evaluated in a grid to estimate the influence of deep moisture on albedo via vegetation cover change; and evaluated the potential of using a two-layer bucket model and empirical relationships to evaluate the link between deep soil moisture and the planetary boundary layer height under changing precipitation regime. My results indicate that (1) the presence or absence of water in two layers plays a role in surface energy dynamics, (2) soil moisture presence in the deep layer is linked with decreased ecosystem albedo and planetary boundary layer height, (3) deep moisture sustains vegetation greenness and decreases albedo, and (4) empirical relationships are useful in modeling planetary boundary layer height from dryland ecosystems. Based on these results we argue that deep soil moisture plays an important role in land surface-atmosphere interactions.

Predicting the interactions between climate change and ecosystems remains a core problem in global change research; tropical forest ecosystems are of particular importance because of their disproportionate role in global carbon and water cycling. Amazonia is unique among tropical forest ecosystems, exhibiting a high degree of coupling with its regional hydrometeorology, such that the stability of the entire forest-climate system is dependent on the functioning of its component parts. Belowground ecohydrological interactions between soil moisture environments and the roots which permeate them initiate the water transport pathway to leaf stomata, yet despite the disproportionate role they play in vegetation-atmosphere coupling in Amazonian forest ecosystems, the impacts of climate variability on the belowground environment remain understudied. The research which follows is designed to address critical knowledge gaps in our understanding of root functioning in Amazonian tropical forests as it relates to seasonality and extremes in belowground moisture regime as well as discerning which ecohydrological mechanisms govern ecosystem-level processes of carbon and water flux. A secondary research theme is the evaluation and use of models of ecosystem function as applied to Amazonia - these models are the "knowledge boxes" which build in the ecohydrological hypotheses (some testable than others) deemed to be most important for the forest ecosystems of Amazonia. In what follows, I investigate (i) which mechanisms of water supply (from the soil environment) and water demand (by vegetation) regulate the magnitude and seasonality of evapotranspiration across broad environmental gradients of Amazonia, (ii) how specific hypotheses of root function are or are not corroborated by soil moisture measurements conducted under normal seasonal and experimentally-induced extreme drought conditions, and (iii) the linkage between an extreme drought event with associated impacts on root zone soil moisture, the inferred response of root water uptake, and the observed impacts on ecosystem carbon and water flux in an east central Amazonian forest.

Made available in DSpace on 2015-03-26T13:50:12Z (GMT). No. of bitstreams: 1 texto completo.pdf: 2283755 bytes, checksum: 7b17f9a16dc883593adc1fe443b6a654 (MD5) Previous issue date: 2013-03-20 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / The performance of LSEMs (Land surface and ecosystem models) depends on the parameters of the equations representing the simulated process. However, the measurement of some parameters can be impractical or even impossible; therefore, they need to be estimated, or preferably optimized specifically for each ecosystem. When the parameters are calibrated to a single variable (mono-objective problem) they may not represent the reality, because the complexity of the model and its dependence on several variables (multi-objective problem). Thus, simultaneous multi-objective optimizations are indispensable. However, the optimization performance decreases as the number of variables to be optimized simultaneously increases. Furthermore, the study of simultaneous optimization using more than three objectives is a new area and not yet sufficiently studied. For simultaneous optimization of a large number of variables, there is a method that uses concepts of hierarchical systems theory in which the optimization occurs from the fastest (radiative fluxes) to the slowest process (carbon allocation). This study evaluates the performance of the hierarchical optimization using the index D (the average of the ratios between the individual outputs of multi-objective optimization and monoobjective). Understanding how the performance index D varies with respect to the number of objective functions optimized and to the number of hierarchical levels is important for the development of this research area. Two steps are necessary to achieve the study goals. First, a sensitivity analysis was performed to determine the output variables sensitivity to the model parameters. After, simulations were made using all possible combinations among the seven micrometeorological variables available (PARo, fAPAR, Rn, u *, H, LE, NEE) taking into account the hierarchy of processes. The results indicate that for up to three objective functions, hierarchical multi-objective optimization generates better results than the simultaneous multiobjective optimization (one hierarchical level), provided that the parameters distribution among hierarchical levels is consistent with the sensitivity analysis. Another important result shows that for the same number of outputs optimized, the greater the number of hierarchical levels the better the performance of the optimized model. However, the model performance falls quickly as the number of objective functions increases, evidencing that the power of hierarchy calibration that use a high number of objective functions is highly dependent on the removal of some constraints for model s performance. / O desempenho de um LSEM (Modelo de superfície terrestre e ecossistema) depende dos parâmetros das equações que representam os processos simulados. Contudo, a mensuração de alguns destes parâmetros pode ser impraticável ou até mesmo impossível; por isso, necessitam ser estimados ou, preferencialmente, otimizados para cada ecossistema. Quando os parâmetros são calibrados para uma única variável (problema mono-objetivo) eles podem não representar bem a realidade, dado a complexidade do modelo e sua dependência de diversas variáveis (problema multiobjetivo). Por isso, há a necessidade de uma otimização simultânea multiobjetiva. Porém, o desempenho da otimização diminui com o aumento do número de variáveis otimizadas simultaneamente e, além disso, o estudo da otimização simultânea de mais de três objetivos é uma área relativamente nova e não suficientemente estudada. Para a otimização simultânea de um grande número de variáveis, existe uma metodologia na qual se utiliza conceitos de teoria hierárquica de sistemas em que a otimização ocorre dos processos mais rápidos (fluxos radiativos) para os mais lentos (alocação de carbono). Este trabalho avalia o desempenho da otimização hierárquica do modelo, utilizando o índice D (a média das razões individuais entre as saídas das otimizações multiobjetiva e monoobjetiva). Entender como o índice de desempenho D do algoritmo de otimização hierárquico varia em relação ao número de funções objetivo otimizadas é de extrema importância para o desenvolvimento desta área de pesquisa. Para fazer atingir os objetivos, foram necessárias duas etapas. Primeiramente, foi feita uma análise de sensibilidade, a fim de conhecer a sensibilidade das variáveis de saída aos parâmetros do modelo. Depois, foram feitas simulações com todas as combinações possíveis entre as sete variáveis micrometeorológicas disponíveis (PARo, fAPAR, Rn, u*, H, LE, NEE) levando em consideração a hierarquia dos processos. Os resultados encontrados indicam que, para até três funções objetivo, a otimização multiobjetiva hierárquica pode gerar resultados melhores do que a otimização multiobjetiva tradicional (um único nível hierárquico), desde que a distribuição dos parâmetros entre as variáveis seja feita de forma coerente com a análise de sensibilidade. Outro resultado importante revela que para um mesmo número de saídas otimizadas, quanto maior o número de níveis hierárquicos melhor o desempenho do modelo otimizado. Porém, o desempenho do modelo diminui rapidamente quando o número de funções objetivo aumenta, evidenciando que o poder da calibração hierárquica para o uso de um grande número de funções objetivo é altamente dependente de algumas restrições que o modelo possui e um alto desempenho do modelo para muitas funções objetivo será possível somente após a remoção delas.