8 resultados para Semi-arid conditions
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This paper describes the palaeoweathering, cementation, clay minerals association and other closely related characteristics of central Portugal allostratigraphic Tertiary units (SLD's), that can be used for palaeoclimatic interpretation and palaeoenvironmental reconstruction. Lateral and vertical changes in palaeosols are of value for improving our understanding of the autocyclic and allocyclic controls on sediment acumulation in an alluvial basin, but they can also have stratigraphic importance. In some cases it is concluded that the geomorphological setting may have been more decisive than climatic conditions to the production of the palaeoweathering. During late Palaeogene (SLD7-8), surface and near-surface silicification were developed on tectonically stable land surfaces of minimal local relief under a semi-arid climate; groundwater flow was responsible for some eodiagenesis calcareous accumulations, with the neoformation of palygorskite. Conditions during the Miocene (SLD9-11) were favourable for the smectization of the metamorphic basement and arenization of granites. Intense rubefaction associated with basement conversion into clay (illite and kaolinite), is ascribed to internal drainage during late Messinian-Zanclean (SLD12). During Piacenzian (SLD13) intense kaolinization and hydromorphism are typical, reflecting a more humid and hot temperate climate and important Atlantic fluvial drainage. Later on (Gelasian-early Pleistocene ?; SLD14). more cold and dry conditicns are interpreted, at the beginning of the fluvial incision sage. Silica cementation is identified in the upper Eocence-Oligocene ? (SLD18; the major period of silicification), middle to upper Miocene (SLD10)and upper Tortonian-Messinian (SLD11); these occurrences are compatible with either arid or semi-arid conditions and the establishment of a flat landscape upon which a silcrete was developed.
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Desertification is a critical issue for Mediterranean drylands. Climate change is expected to aggravate its extension and severity by reinforcing the biophysical driving forces behind desertification processes: hydrology, vegetation cover and soil erosion. The main objective of this thesis is to assess the vulnerability of Mediterranean watersheds to climate change, by estimating impacts on desertification drivers and the watersheds’ resilience to them. To achieve this objective, a modeling framework capable of analyzing the processes linking climate and the main drivers is developed. The framework couples different models adapted to different spatial and temporal scales. A new model for the event scale is developed, the MEFIDIS model, with a focus on the particular processes governing Mediterranean watersheds. Model results are compared with desertification thresholds to estimate resilience. This methodology is applied to two contrasting study areas: the Guadiana and the Tejo, which currently present a semi-arid and humid climate. The main conclusions taken from this work can be summarized as follows: • hydrological processes show a high sensitivity to climate change, leading to a significant decrease in runoff and an increase in temporal variability; • vegetation processes appear to be less sensitive, with negative impacts for agricultural species and forests, and positive impacts for Mediterranean species; • changes to soil erosion processes appear to depend on the balance between changes to surface runoff and vegetation cover, itself governed by relationship between changes to temperature and rainfall; • as the magnitude of changes to climate increases, desertification thresholds are surpassed in a sequential way, starting with the watersheds’ ability to sustain current water demands and followed by the vegetation support capacity; • the most important thresholds appear to be a temperature increase of +3.5 to +4.5 ºC and a rainfall decrease of -10 to -20 %; • rainfall changes beyond this threshold could lead to severe water stress occurring even if current water uses are moderated, with droughts occurring in 1 out of 4 years; • temperature changes beyond this threshold could lead to a decrease in agricultural yield accompanied by an increase in soil erosion for croplands; • combined changes of temperature and rainfall beyond the thresholds could shift both systems towards a more arid state, leading to severe water stresses and significant changes to the support capacity for current agriculture and natural vegetation in both study areas.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Dissertation for obtaining the Master degree in Membrane Engineering
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Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.
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Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia do Ambiente, Gestão de Sistemas Ambientais
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Mestrado integrado em Engenharia do Ambiente, perfil: Gestão de Sistemas Ambientais