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.

abstract: Land surface fluxes of energy and mass developed over heterogeneous mountain landscapes are fundamental to atmospheric processes. However, due to their high complexity and the lack of spatial observations, land surface processes and land-atmosphere interactions are not fully understood in mountain regions. This thesis investigates land surface processes and their impact on convective precipitation by conducting numerical modeling experiments at multiple scales over the North American Monsoon (NAM) region. Specifically, the following scientific questions are addressed: (1) how do land surface conditions evolve during the monsoon season, and what are their main controls?, (2) how do the diurnal cycles of surface energy fluxes vary during the monsoon season for the major ecosystems?, and (3) what are the impacts of surface soil moisture and vegetation condition on convective precipitation? Hydrologic simulation using the TIN-based Real-time Integrated Basin Simulator (tRIBS) is firstly carried out to examine the seasonal evolution of land surface conditions. Results reveal that the spatial heterogeneity of land surface temperature and soil moisture increases dramatically with the onset of monsoon, which is related to seasonal changes in topographic and vegetation controls. Similar results are found at regional basin scale using the uncoupled WRF-Hydro model. Meanwhile, the diurnal cycles of surface energy fluxes show large variation between the major ecosystems. Differences in both the peak magnitude and peak timing of plant transpiration induce mesoscale heterogeneity in land surface conditions. Lastly, this dissertation examines the upscale effect of land surface heterogeneity on atmospheric condition through fully-coupled WRF-Hydro simulations. A series of process-based experiments were conducted to identify the pathways of soil moisture-rainfall feedback mechanism over the NAM region. While modeling experiments confirm the existence of positive soil moisture/vegetation-rainfall feedback, their exact pathways are slightly different. Interactions between soil moisture, vegetation cover, and rainfall through a series of land surface and atmospheric boundary layer processes highlight the strong land-atmosphere coupling in the NAM region, and have important implications on convective rainfall prediction. Overall, this dissertation advances the study of complex land surface processes over the NAM region, and made important contributions in linking complex hydrologic, ecologic and atmospheric processes through numerical modeling. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2016

abstract: Population growth within drylands is occurring faster than growth in any other ecologic zone, putting pressure on already stressed water resources. Because the availability of surface water supplies in drylands tends to be highly variable, many of these populations rely on groundwater. A critical process contributing to groundwater recharge is the interaction between ephemeral channels and groundwater aquifers. Generally, it has been found that ephemeral channels contribute to groundwater recharge when streamflow infiltrates into the sandy bottoms of channels. This process has traditionally been studied in channels that drain large areas (10s to 100s km2). In this dissertation, I study the interactions between surface water and groundwater via ephemeral channels in a first-order watershed located on an arid piedmont slope within the Jornada Experimental Range (JER) in the Chihuahuan Desert. To achieve this, I utilize a combination of high-resolution observations and computer simulations using a modified hydrologic model to quantify groundwater recharge and shed light on the geomorphic and ecologic processes that affect the rate of recharge. Observational results indicate that runoff generated within the piedmont slope contributes significantly to deep percolation. During the short-term (6 yr) study period, we estimated 385 mm of total percolation, 62 mm/year, or a ratio of percolation to rainfall of 0.25. Based on the instrument network, we identified that percolation occurs inside channel areas when these receive overland sheetflow from hillslopes. By utilizing a modified version of the hydrologic model, TIN-based Real-time Integrated Basin Simulator (tRIBS), that was calibrated and validated using the observational dataset, I quantified the effects of changing watershed properties on groundwater recharge. Distributed model simulations quantify how deep percolation is produced during the streamflow generation process, and indicate that it plays a significant role in moderating the production of streamflow. Sensitivity analyses reveal that hillslope properties control the amount of rainfall necessary to initiate percolation while channel properties control the partitioning of hillslope runoff into streamflow and deep percolation. Synthetic vegetation experiments show that woody plant encroachment leads to increases in both deep percolation and streamflow. Further woody plant encroachment may result in the unexpected enhancement of dryland aquifer sustainability. / Dissertation/Thesis / Doctoral Dissertation Geological Sciences 2017

abstract: This dissertation research studies long-term spatio-temporal patterns of surface urban heat island (SUHI) intensity, urban evapotranspiration (ET), and urban outdoor water use (OWU) using Phoenix metropolitan area (PMA), Arizona as the case study. This dissertation is composed of three chapters. The first chapter evaluates the SUHI intensity for PMA using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product and a time-series trend analysis to discover areas that experienced significant changes of SUHI intensity between 2000 and 2017. The heating and cooling effects of different urban land use land cover (LULC) types was also examined using classified Landsat satellite images. The second chapter is focused on urban ET and the impacts of urban LULC change on ET. An empirical model of urban ET for PMA was built using flux tower data and MODIS land products using multivariate regression analysis. A time-series trend analysis was then performed to discover areas in PMA that experienced significant changes of ET between 2001 and 2015. The impact of urban LULC change on ET was examined using classified LULC maps. The third chapter models urban OWU in PMA using a surface energy balance model named METRIC (Mapping Evapotranspiration at high spatial Resolution with Internalized Calibration) and time-series Landsat Thematic Mapper 5 imagery for 2010. The relationship between urban LULC types and OWU was examined with the use of very high-resolution land cover classification data generated from the National Agriculture Imagery Program (NAIP) imagery and regression analysis. Socio-demographic variables were selected from census data at the census track level and analyzed against OWU to study their relationship using correlation analysis. This dissertation makes significant contributions and expands the knowledge of long-term urban climate dynamics for PMA and the influence of urban expansion and LULC change on regional climate. Research findings and results can be used to provide constructive suggestions to urban planners, decision-makers, and city managers to formulate new policies and regulations when planning new constructions for the purpose of sustainable development for a desert city. / Dissertation/Thesis / Doctoral Dissertation Geography 2018

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The following study quantifies the seasonal and annual distribution of energy balance components (sensible heat fluxes, latent, soil, and net radiation) in this flooded irrigated rice ecosystem in Southern Brazil for three different periods (Fallow 1: 22 July 2003 to 24 November 2003; Rice: 25 November 2003 to 04 April 2004 and Fallow 2: 05 April 2004 to 21 July 2004). In addition, it has been applied the Noah Land Surface Model with the objective of estimating the surface energy fluxes. An important challenge is to implement a new version of Noah Land Surface Model applied to flooded agricultural land called Noah-Paddy. The stabilization of the models has been performed using the atmospheric forcing data obtained from South American Land Data Assimilation System (SALDAS) for the period 22 July 2000 to 21 July 2003. The models were simulated using the observed atmospheric forcing from a micrometeorological tower installed on a flooded irrigated rice paddies located in the city of Paraíso do Sul - RS. The initial conditions were obtained from the last time step of the spin-up experiment performed with atmospheric forcing data of SALDAS. The models results were compared with experimental data for surface energy fluxes. From the simulated results generated by the Noah Land Surface Model, it seems that when the rice crop is flooded, the model does not satisfactorily represents the experimental data. However, using the Noah-Paddy model the components of surface energy balance are more realistic for the system surface-water-atmosphere. The most important contribution performed in this research was to describe the diffent physical processes originated by the presence of a body of water between the soil surface and the atmosphere. This physical system occorr always in flooded agricultural crops in wich the rice paddies field are predominant. / No presente trabalho, quantifica-se a distribuição sazonal e anual das componentes do balanço de energia (fluxos de calor sensível, latente, do solo e saldo de radiação) em um ecossistema de arroz irrigado por inundação localizado no Sul do Brasil para três períodos distintos ao longo do ano (Pousio 1: 22Jul2003 a 24Nov2003; Arroz: 25Nov2003 a 04Abr2004 e Pousio 2: 05Abr2004 a 21Jul2004). Além disso, é utilizado o Modelo de Superfície Noah (Noah LSM) com o objetivo de estimar os fluxos de energia superficiais. Um dos desafios mais importantes é a implementação de uma nova versão do Noah LSM aplicado para áreas agrícolas alagáveis chamado Noah-Paddy. A estabilização dos modelos foi realizada utilizando dados de forçantes atmosféricas do South American Land Data Assimilation System (SALDAS) para o período de 22Jul2000 a 21Jul2003. Os modelos foram executados usando dados de forçantes atmosféricas observados obtidos a partir da torre micrometeorológica instalada sobre uma cultura de arroz irrigado por inundação localizada em Paraíso do Sul - RS. As condições iniciais foram obtidas a partir do último passo de tempo do experimento spin-up realizado com os dados de forçantes atmosféricas do SALDAS. O desempenho dos modelos estudados foi comparado com dados experimentais de fluxos de energia superficiais. A partir dos resultados obtidos pela simulação do Noah LSM verifica-se que, quando a cultura do arroz está irrigada, o modelo não representa satisfatoriamente os dados experimentais. Porém, utilizando o Noah-Paddy as trocas de energia superficiais são representadas de forma mais realísticas para o sistema superfície-água-atmosfera. A contribuição mais importante realizada neste trabalho foi a descrição dos diferentes processos físicos originados pela presença de uma massa de água entre a superfície do solo e a atmosfera. Esse sistema físico ocorre sempre em culturas agrícolas alagadas nas quais as plantações de arroz são predominantes.

Un modèle de surface continentale (LSM en anglais) est un modèle numérique décrivant les échanges d'eau et d'énergie entre la surface terrestre et l'atmosphère. La physique de la surface de la terre comprend une vaste collection de processus complexes. L'équilibre entre la complexité du modèle et sa résolution, confronté à des limitations de calcul, représente une question fondamentale dans le développement d'un LSM. Les observations des phénomènes étudiés sont nécessaires afin d’adapter la valeur des paramètres du modèle à des variables reproduisant le monde réel. Le processus d'étalonnage consiste en une recherche des paramètres du modèle qui minimisent l’écart entre les résultats du modèle et un ensemble d'observations. Dans ce travail, nous montrons comment l'assimilation variationnelle de données est appliquée aux bilans d'énergie et d'eau du modèle de surface continentale ORCHIDEE afin d’étalonner les paramètres internes du modèle. Cette partie du modèle est appelé SECHIBA. Le logiciel YAO est utilisé pour faciliter la mise en œuvre de l'assimilation variationnelle 4DVAR. Une analyse de sensibilité a été réalisée afin d'identifier les paramètres les plus influents sur la température. Avec la hiérarchie des paramètres obtenue, des expériences jumelles à partir d'observations synthétiques ont été mises en œuvre. Les résultats obtenus suggèrent que l'assimilation de la température de surface a le potentiel d'améliorer les estimations de variables, en ajustant correctement les paramètres de contrôle. Enfin, plusieurs assimilations ont été faites en utilisant des observations de données réelles du site SMOSREX à Toulouse, France. Les expériences faites en utilisant différentes valeurs initiales pour les paramètres, montrent les limites de l'assimilation de la température pour contraindre les paramètres de contrôle. Même si l'estimation des variables est améliorée, ceci est dû à des valeurs finales des paramètres aux limites des intervalles prescrit de la fonction de coût. Afin de parvenir à un minimum, il faudrait permettre aux paramètres de visiter des valeurs irréalistes. Les résultats montrent que SECHIBA ne simule pas correctement simultanément la température et les flux et la relation entre les deux n’est pas toujours cohérente selon le régime (ou les valeurs des paramètres que l’on utilise). Il faut donc travailler sur la physique pour mieux simuler la température. En outre, la sensibilité des paramètres à la température n’est pas toujours suffisante, donnant une fonction de coût plate dans l’espace des paramètres prescrit. Nos résultats montrent que le système d'assimilation mis en place est robuste, puisque les résultats des expériences jumelles sont satisfaisants. Le couplage entre l'hydrologie et la thermodynamique dans SECHIBA doit donc être revu afin d'améliorer l'estimation des variables. Une étude exhaustive de l'erreur des mesures doit être menée afin de récupérer des termes de pondération dans la fonction de coût. Enfin, l'assimilation d'autres variables telles que l'humidité du sol peut maintenant être réalisée afin d'évaluer l'impact sur les performances de l’assimilation. / A land surface model (LSM) is a numerical model describing the exchange of water and energy between the land surface and the atmosphere. Land surface physics includes an extensive collection of complex processes. The balance between model complexity and resolution, subject to computational limitations, represents a fundamental query in the development of a LSM. With the purpose of adapting the value of the model parameters to values that reproduces results in the real world, measurements are necessary in order to compare to our estimations to the real world. The calibration process consists in an optimization of model parameters for a better agreement between model results and a set of observations, reducing the gap between the model and the available measurements. Here we show how variational data assimilation is applied to the energy and water budgets modules of the ORCHIDEE land surface model in order to constrain the model internal parameters. This part of the model is denoted SECHIBA. The adjoint semi-generator software denoted YAO is used as a framework to implement the 4DVAR assimilation. A sensitivity analysis was performed in order to identify the most influent parameters to temperature. With the parameter hierarchy resolved, twin experiments using synthetic observations were implemented for controlling the most sensitive parameters. Results obtained suggest that land surface temperature assimilation has the potential of improving the output estimations by adjusting properly the control parameters. Finally, several assimilations were made using observational meteorology dataset from the SMOSREX site in Toulouse, France. The experiments implemented, using different prior values for the parameters, show the limits of the temperature assimilation to constrain control parameters. Even though variable estimation is slightly improved, this is due to final parameter values are at the edge of a variation interval in the cost function. Effectively reaching a minimum would require allowing the parameters to visit unrealistic values. SECHIBA does not correctly simulates simultaneously temperature and fluxes and the relationship between the two is not always consistent according to the regime (or parameter values that are used). We must therefore work on the physical aspects to better simulate the temperature. Likewise, the parameter sensitivity to temperature is not always sufficient, giving as a result a flat cost function. Our results show that the assimilation system implemented is robust, since performances results in twin experiments are satisfactory. The coupling between the hydrology and the thermodynamics in SECHIBA must be reviewed in order to improve variable estimation. An exhaustive study of the prior errors in the measurements must be conducted in order to retrieve more adapted weighing terms in the cost function. Finally, the assimilation of other variables such as soil moisture should be performed to evaluate the impacts in constraining control parameters

L'évolution naturelle du climat, perturbée depuis les révolutions industrielles, est fortement marquée dans les hautes latitudes en particulier en Sibérie où une anomalie de température de +0.8°C est constatée depuis les années 2000 contre une anomalie moyenne de +0.4°C pour les moyennes latitudes. La Sibérie est couverte par des pergélisols lui conférant ainsi des particularités, notamment pour les régimes hydrologiques des rivières. Les projections climatiques prédisant jusqu'à un réchauffement de l'ordre de +5°C d'ici 2100, il est primordial d'en évaluer les impacts. La modélisation numérique à bases physiques s'avère être un outil intéressant pour répondre à ces questions. Ainsi, afin d'évaluer la réponse hydrologique au changement climatique en Sibérie nous avons travaillé sur l'évaluation multi-échelles des bilans d'énergie et d'eau avec le modèle ORCHIDEE. Ce modèle a été adapté aux caractéristiques des milieux froids, avec une amélioration de la représentation de la neige, une prise en compte du gel de l'eau du sol et une carte de végétation plus représentative de la végétation sibérienne. Une évaluation en mode forcé i.e. sans couplage avec l'atmosphère a été menée dans un premier temps. Ainsi, nous avons évalué ORCHIDEE au temps présent (1979-2009) à l'échelle du site en nous concentrant sur les données d'humidité et de température du sol dont nous disposions. Une analyse de sensibilité du modèle nous a permis d'identifier les paramètres les plus influents sur les bilans d'énergie et d'eau dans le sol. Leur étalonnage sur sites nous a permis de montrer que le modèle ORCHIDEE est capable de simuler correctement les transferts verticaux de chaleur et d'eau et les contenus en eau et températures du sol résultants. Par la suite nous avons étendu l'évaluation à la région de la Sibérie en confrontant nos résultats de simulation à des produits satellitaires, permettant une évaluation sur une série temporelle conséquente et sur une grande zone. Nous avons rassemblé un grand nombre d'observations telles que des données d'albédo, d'équivalent en eau pour la neige..., auxquelles nous avons comparé nos résultats de simulation. Ce travail nous a permis de montrer que le modèle simule de façon satisfaisante les bilans d'énergie et d'eau en Sibérie, mais aussi de mettre en avant l'importance du choix du forçage climatique. Ainsi, l'utilisation d'un second forçage climatique nous a permis de montrer l'importance du partitionnement pluie/neige ainsi que la sous-estimation possible des précipitations dans les forçages. Le modèle validé a été utilisé ensuite pour mener des études d'impacts, en utilisant 2 forçages climatiques sur le temps futur (2005 à 2099) sous scénario d'émission des gaz à effet de serre RCP8.5. Ainsi nous avons pu évaluer la variabilité liée au forçage et l'impact de l'évolution du climat sur les variables des bilans d'énergie et d'eau. Une limite autour de la latitude 60°N a été définie lors de l'analyse des précipitations futures et choisie pour orienter notre analyse selon deux zones de part et d'autre de la limite. Nous avons analysé les cycles saisonniers des variables de surface nous permettant de mettre en évidence les impacts du réchauffement climatique en lien avec l'augmentation de la température de l'air et leurs différences spatiales. Nous avons montré que la fonte du manteau neigeux est plus précoce au Sud et engendre une avance temporelle du pic de crue de printemps pour la Lena et l'Amour. Sur l'Ob et le Ienisseï, des changements ont été aussi montrés (une diminution du débit au cours du temps pour l'Ob et une augmentation pour le Ienisseï, sans changement de phasage temporel), qui pourraient conduire à des impacts socio-économiques importants pour les populations locales. Cette étude nous a également permis de montrer que les nouvelles conditions climatiques sont plus favorables à la végétation. Nous avons montré aussi la cohérence des deux projections climatiques étudiées. / The natural evolution of the climate, disturbed since the industrial revolutions, is strongly marked in the high latitudes especially in Siberia where a temperature anomaly of +0.8°C has been observed since the 2000s against an average anomaly of + 0.4°C for The mid-latitudes. Siberia is covered by permafrost, giving it particularities, especially for the hydrological regimes of rivers. Climatic projections predicting up to +5°C warming by 2100, it is essential to evaluate their impacts. Physical-based numerical modeling is an interesting tool to answer these questions. Thus, in order to evaluate the hydrological response to climate change in Siberia we worked on the multi-scale evaluation of energy and water balances with the ORCHIDEE model. This model was adapted to the characteristics of cold environments, with an improvement of the representation of the snow, a consideration of the freezing of the soil water and a map of vegetation more representative of the Siberian vegetation. An evaluation in forced mode i.e. without coupling with the atmosphere was carried out initially. Thus, we evaluated ORCHIDEE at the present time (1979-2009) at the site scale, concentrating on the soil moisture and soil temperature data available. A sensitivity analysis of the model allowed to identify the most influential parameters on the balance of energy and water in the soil. Their on-site calibration allowed to show that the ORCHIDEE model is able to correctly simulate the vertical transfers of heat and water and the resulting water and soil temperature contents. We then extended the evaluation to the Siberian region by comparing simulation results with remote sensing data, allowing an evaluation over a substantial time series and over a large area. We collected a large number of observations such as albedo data, water equivalent for snow ..., on which we compared the simulation results. This work allowed to show that the model simulates satisfactorily the energy and water balance in Siberia, but also to highlight the importance of the choice of climatic forcing. Thus, the use of a second climatic forcing enabled to show the importance of rain/snow partitioning and the possible underestimation of precipitation in forcing. The validated model was then used to carry out impact studies, using 2 climatic forcings on the future time (2005 to 2099) under scenario of emission of greenhouse gases RCP8.5. Thus, we were able to evaluate the variability related to forcing and the impact of climate change on the variables of energy and water balance. A boundary around latitude 60°N has been defined in the analysis of future precipitation and chosen to orient our analysis in two zones on either side of the boundary. We analyzed the seasonal cycles of the surface variables allowing us to highlight the impacts of global warming in relation to the increase in the air temperature and their spatial differences. We have shown that the melting of the snowpack is earlier in the South and generates a temporal advance of the spring flood peak for the Lena and the Amur. On the Ob and Yenisei, changes have also been shown (a decrease in flow over time for the Ob and an increase for the Yenisei, without any change in temporal phasing), which could lead to socio-economic impacts Important for local populations. This study also allowed us to show that the new climatic conditions are more favorable to vegetation. We also showed the coherence of the two climate projections studied.

Land surface temperature (LST) and mean radiant temperature (MRT) are commonly used as proxies to evaluate urban heat environments. Many scholars use one of them to represent heat exposure when assessing the urban thermal environment. This research fills a research gap by analyzing two meteorological parameters simultaneously through correlation analysis, hotspot analysis, and the distinctive information they respectively express with the results of vulnerable population distribution based on the case of Linköping. Scatter plots are used to explore the correlation between LST and MRT, and hot spot analysis is applied to investigate their spatial patterns through the clusters of hot and cold spots. Furthermore, the distribution of vulnerable populations is assessed and visualized through a vulnerability index. The results show that there is a moderate positive linear correlation between the mean values of LST and MRT for the whole study area. They have different spatial patterns based on the results of the hot spot analysis. The comparison of different meteorological parameters to the vulnerability index also shows variations in high heat risk areas. All of these conclude that LST or MRT could, to some extent, be presented as references to each other; however, they cannot be used interchangeably as proxies for urban heat exposure. When developing urban thermal adaptation strategies, it is necessary for municipalities to select the parameters appropriately according to the purpose and requirements and to understand what the chosen parameters can and cannot convey.

In 2005, Hurricane Katrina’s diverse impacts on the Greater New Orleans area included damaged and destroyed trees, and other despoiled vegetation, which also increased the exposure of artificial and bare surfaces, known factors that contribute to the climatic phenomenon known as the urban heat island (UHI). This is an investigation of UHI in the aftermath of Hurricane Katrina, which entails the analysis of pre and post-hurricane Katrina thermal imagery of the study area, including changes to surface heat patterns and vegetative cover. Imagery from Landsat TM was used to show changes to the pattern and intensity of the UHI effect, caused by an extreme weather event. Using remote sensing visualization methods, field data, and local knowledge, the author found there was a measurable change in the pattern and intensity of the New Orleans UHI effect, as well as concomitant changes to vegetative land cover. This finding may be relevant for urban planners and citizens, especially in the context of recovery from a large-scale disaster of a coastal city, regarding future weather events, and other natural and human impacts.

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.