1985-2000

Caetano, M., Carrao, H., & Painho, M. (2005). Alterações da ocupação do espaço do solo em Portugal Continental: 1985-2000. Lisboa: Instituto do Ambiente.Projecto realizado para o Instituto do Ambiente, no âmbito do protocolo celebrado entre esta entidade e o Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa (ISEGI-UNL).publishersversionpublishe

Strategic Green Infrastructure and Ecosystem Restoration

This report draws on a range of European-wide datasets, geospatial methods, and tools available for green infrastructure (GI) mapping. It shows how two complementary mapping approaches (physical and ecosystem based) and the three key GI principles of connectivity, multifunctionality and spatial planning are used in case studies selected in urban and rural landscapes; it provides guidance for the strategic design of a well-connected, multi-functional, and cross-border GI, and identifies knowledge gaps. GI mapping has been demonstrated to enhance nature protection and biodiversity beyond protected areas, to deliver ecosystem services such as climate change mitigation and recreation, to prioritise measures for defragmentation and restoration in the agri-environment and regional development context, and to find land allocation trade-offs and possible scenarios involving all sectors.JRC.D.6-Knowledge for Sustainable Development and Food Securit

Mapping global patterns of drought risk: an empirical framework based on sub-national estimates of hazard, exposure and vulnerability

A global map of drought risk has been elaborated at the sub-national administrative level. The motivation for this study is the observation that little research and no concerted efforts have been made at the global level to provide a consistent and equitable drought risk management framework for multiple regions, population groups and economic sectors. Drought risk is assessed for the period 2000-2014 and is based on the product of three independent determinants: hazard, exposure and vulnerability. Drought hazard is derived from a non-parametric analysis of historical precipitation deficits at the 0.5dd; drought exposure is based on a non-parametric aggregation of gridded indicators of population and livestock densities, crop cover and water stress; and drought vulnerability is computed as the arithmetic composite of high level factors of social, economic and infrastructural indicators, collected at both the national and sub-national levels. The performance evaluation of the proposed models underlines their statistical robustness and emphasizes an empirical resemblance between the geographic patterns of potential drought impacts and previous results presented in the literature. Our findings support the idea that drought risk is driven by an exponential growth of regional exposure, while hazard and vulnerability exhibit a weaker relationship with the geographic distribution of risk values. Drought risk is lower for remote regions, such as tundras and tropical forests, and higher for populated areas and regions extensively exploited for crop production and livestock farming, such as South-Central Asia, Southeast of South America, Central Europe and Southeast of the United States. As climate change projections foresee an increase of drought frequency and intensity for these regions, then there is an aggravated risk for global food security and potential for civil conflict in the medium- to long-term. Since most agricultural regions show high infrastructural vulnerability to drought, then regional adaption to climate change may begin through implementing and fostering the widespread use of irrigation and rainwater harvesting systems. In this context, reduction in drought risk may also benefit from diversifying regional economies on different sectors of activity and reducing the dependence of their GDP on agriculture.JRC.H.7-Climate Risk Managemen

Agricultural Drought Assessment in Latin America Based on a Standardized Soil Moisture Index

We propose a relatively simple, spatially invariant and probabilistic year-round Standardized Soil Moisture Index (SSMI) that is designed to estimate drought conditions from satellite imagery data. The SSMI is based on soil moisture content alone and is defined as the number of standard deviations that the observed moisture at a given location and timescale deviates from the longterm normal conditions. Specifically, the SSMI is computed by fitting a non-parametric probability distribution function to historical soil moisture records and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry conditions and positive values indicate wet conditions. To evaluate the applicability of the SSMI, we fitted empirical and normal cumulative distribution functions (ECDF and nCDF) to 32-years of averaged soil moisture amounts derived from the Essential Climate Variable (ECV) Soil Moisture (SM) dataset, and compared the root-mean-squared errors of residuals. SM climatology was calculated on a 0:25 grid over Latin America at timescales of 1, 3, 6, and 12 months for the long-term period of 1979-2010. Results show that the ECDF fits better the soil moisture data than the nCDF at all timescales and that the negative SSMI values computed with the non-parametric estimator accurately identified the temporal and geographic distribution of major drought events that occurred in the study area.JRC.H.7-Climate Risk Managemen

Towards identifying areas at climatological risk of desertification using the Köppen-Geiger classification and FAO aridity index

Over the past decades, a continuous rise in global air temperatures resulted in significant changes in the global hydrological cycle. Regionally increased frequencies of extreme weather events and changes in the regional extent of drylands resulted in new areas at risk of desertification, a complex process driven by socio-economic and climate-related factors. Although desertification is not confined to drylands, they are the most vulnerable to land degradation processes. To investigate possible changes in climate patterns over the past 60 years, we couple the information obtained from the Köppen-Geiger (KG) climate classification and the FAO aridity index (AI), providing an overview of the most evident global changes in climate regimes from 1951-1980 to 1981-2010 and focussing on the modifications of the extent of drylands. KG and AI indicators have been computed on a 0.5º x0.5º global grid using precipitation data from the Full Data Reanalysis (v6.0) of the Global Precipitation Climatology Centre, and mean temperature and potential evapotranspiration data from the Climate Research Unit of the University of East Anglia (CRUTSv3.20). Both KG and AI show that the arid areas globally increased between 1951-1980 and 1981-2010, but decreased on average in the Americas. North-Eastern Brazil, Southern Argentina, the Sahel, Zambia and Zimbabwe, the Mediterranean area, North-Eastern China and Sub-Himalayan India have been identified as areas with a significant increase of drylands extent. An analysis of the scientific literature gives evidence that most of the areas identified are effectively undergoing desertification, thus confirming the validity of AI and KG to highlight the areas under risk of desertification. We also discuss the global decrease of cold areas, the progressive change from continental to temperate climate in Central Europe, the shift from tundra to continental climate in Alaska, Canada and North-Eastern Russia and the widening of the tropical belt.Fil: Spinoni, Jonathan. Institute for Environment and Sustainability; ItaliaFil: Vogt, Jürgen. Institute for Environment and Sustainability; ItaliaFil: Naumann, Gustavo. Institute for Environment and Sustainability; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carrao, Hugo. Institute for Environment and Sustainability; ItaliaFil: Barbosa, Paulo. Institute for Environment and Sustainability; Itali

An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

We propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Mois-ture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellitesurface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonpara-metric empirical probability density function (ePDF) to historical time-series of soil moisture observationsand then transforming it into a normal distribution with a mean of zero and standard deviation of one.Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soilconditions. Drought intensity is defined as the number of negative standard deviations between theobserved soil moisture value and the respective normal climatological conditions. To evaluate the per-formance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0data values collected in the period between January 1981 and December 2010 at South–Central America,and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal proba-bility density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-seriesof ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDFprovides triggers of agricultural drought onset and intensity that are more accurate and precise than thebPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over thelast three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to providea well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheatcrop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed duringthe months of critical crop growing, indicating that the nonparametric index of soil moisture anomaliescan be used for agricultural drought assessment.JRC.H.7-Climate Risk Managemen

Seasonal drought forecasting for Latin America using the ECMWF S4 forecast system

Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF) seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated). Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points at the North of South America, Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does captures reasonably well the occurrence of drought events for some regions and months. In the near term, the largest advances in the prediction of meteorological drought for Latin America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.JRC.E.1-Disaster Risk Managemen

Magnitude of extreme heat waves in present climate and their projection in a warming world

An extreme heat wave occurred in Russia in the summer of 2010. It had serious impacts on humans and natural ecosystems, it was the strongest recorded globally in recent decades and exceeded in amplitude and spatial extent the previous hottest European summer in 2003. Earlier studies have not succeeded in comparing the magnitude of heat waves across continents and in time. This study introduces a new Heat Wave Magnitude Index (HWMI) that can be compared over space and time. The index is based on the analysis of daily maximum temperature, in order to classify the strongest heat waves that occurred worldwide during the three study periods 1980-1990, 1991-2001 and 2002-2012. In addition, multi-model ensemble outputs from the Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves, under different Representative Concentration Pathways (RCP), adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report (AR5). Results show that the percentage of global area affected by heat waves has increased in recent decades. Moreover, model predictions reveal an increase in the probability of occurrence of extreme and very extreme heat waves in the coming years: in particular, by the end of this century, under the most severe IPCC AR5 scenario, events of the same severity as that in Russia in the summer of 2010 will become the norm and are projected to occur as often as every two years for regions such as southern Europe, North America, South America, Africa and Indonesia.JRC.H.7-Climate Risk Managemen