3 resultados para seasonal climate prediction
em Repositório Científico da Universidade de Évora - Portugal
Resumo:
Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species’ distributions under climate and land-use change
Resumo:
We used 2012 sap flow measurements to assess the seasonal dynamics of daily plant transpiration (ETc) in a high-density olive orchard (Olea europaea L. cv. ‘Arbequina’) with a well-watered (HI) control treatment A to supply 100 % of the crop water needs, and a moderately (MI) watered treatment B that replaced 70% of crop needs. To assure that treatment A was well-watered, we compared field daily ETc values against ETc obtained with the Penman-Monteith (PM) combination equation incorporating the Orgaz et al. (2007) bulk daily canopy conductance (gc) model, validated for our non-limiting conditions. We then tested the hypothesis of indirectly monitoring olive ETc from readily available vegetation index (VI) and ground-based plant water stress indicator. In the process we used the FAO56 dual crop coefficient (Kc) approach. For the HI olive trees we defined Kcb as the basal transpiration coefficient, and we related Kcb to remotely sensed Soil Adjusted Vegetation Index (SAVI) through a Kcb-SAVI functional relationship. For the MI treatment, we defined the actual transpiration ETc as the product of Kcb and the stress reduction coefficient Ks obtained as the ratio of actual to crop ETc, and we correlated Ks with MI midday stem water potential (ψst) values through a Ks-ψ functional relationship. Operational monitoring of ETc was then implemented with the ETc = Kcb(SAVI)Ks(ψ)ETo relationship stemmed from the FAO56 approach and validated taking as inputs collected SAVI and ψst data reporting to year 2011. Low validation error (6%) and high goodness-of-fit of prediction were observed (R2 = 0.94, RSME = 0.2 mm day-1, P = 0.0015), allowing to consider that under field conditions it is possible to predict ETc values for our hedgerow olive orchards if SAVI and water potential (ψst) values are known.
Resumo:
Climate change projections point to increasing air temperature and reduced precipitation in southern Portugal, which would affect farming systems. This study aims to assess the impacts of climate change on irrigated agriculture in southern Portugal. These impacts were assessed by combining climate model data with a soil water balance model and a numerical model for the design of irrigation systems. Meteorological data from two weather stations were used along with three climate models (HadRM3P, HIRHAMh and HIRHAMhh; 2071–2100). The crop rotations studied included sugar beet–maize–tomato–wheat and sunflower–wheat–barley. Two adaptation measures were considered: (i) maintaining the current crop varieties; (ii) using new crop varieties. The results from the considered climate change scenarios indicated that the impacts of climate change on irrigation requirements depend on the adopted adaptation measures. On average, the seasonal irrigation requirements increased by 13–70% when new crop varieties were used and by −13 to 7% when the current crop varieties were maintained. The impacts of climate change on irrigation system design were considerable, with the design flow rate increasing by 5–24%.