Scenarios of daily temperature and rainfall for ten-years periods for the decades 1970-79, 2030-39 and 2090-2099 for the Agri and Guadalentin MEDALUS Target Areas, based on output from climate models run to simulate the effects of global warming.
A 1 decimal minute resolution land degradation indicator database for the entire Mediterranean climate region of the EU
The database is a compilation of various source data and various data derived from these during the modelling activities undertaken in MEDALUS III
The RDI model provides an estimate of soil erosion risk for southern Europe at 1 km resolution. The risk estimate is of the mean rate of soil erosion from the land, summed over the frequency distribution of storms derived from daily rainfall records, interpolated at 50 km resolution (the MARS database), re-interpolated to 1 km resolution with additional station data where available. The estimates make use of land use data (CORINE and AVHRR-derived estimates provided by SAI, JRC-Ispra), Soils Data (European Soils Data Base), the 1 km DTM (US EROS Data centre). The output can be expressed as runoff threshold (daily rainfall required for widespread runoff to occur) or erosion estimates on a monthly basis. The physically based model can be used to test the impact of climate and landus'e scenarios in the context of policy proposals or global change.
Database of surface meteorological observations for countries in the Mediterranean basin
Database of principal socio-economic factors in the Guadalentin (Murcia, SE Spain) in relations to desertification processes.
The principal aim have been to try to provide an integrated approach to understanding the processes of desertification, taking into account the relevant socioeconomic factors, and developing scientific basis and simples models for rational management of land resources. The main factors analysed have been: demographic factors, land use, agricultural production, farming price, incomes, agricultural subsidies, resources of water, soil salinization by irrigation water, overexploitation of the aquifer, agricultural machinery, fertilizers consumption and modalities of irrigation.
The method is based on the application of a linear spectral mixing model to the NDVI and land surface temperature (Ts) channels of regional scale remote sensing data (e.g. NOAA AVHRR). It allows the derivation of fractional (percent) cover estimates of photosynthetically active vegetation per pixel. The estimate is typically derived in 10 days or monthly intervals of long term time series of satellite data. It allows a rapid estimate of vegetation cover status over large areas and the assessment of vegetation dynamics over longer periods. The derived vegetation cover density maps are, a direct input to the exploitable result No. 1 (RDI model) and to models of vegetation productivity and dynamics.
ICAT group developed a comprehensive downscaling procedure for precipitation, applicable to the Mediterranean Target areas where local rainfall is mainly determined by synoptic-scale weather systems, suitable for impact studies.
The result is a new method for the assessment in economic terms of some of the environmental benefits of soil erosion control projects applied in Mediterranean basins affected by desertification. It is aimed to the national and European level agencies involved in the formulation of policies and budget determination and justification in the field of natural resources restoration and environment conservation.
Seasonal scenarios of the mean change in temperature and rainfall due to global warming across the whole Mediterranean region, at a resolution of 1km by 1 km. These scenarios have been constructed for the present day, and for two future decades, 2030-39 and 2090-99.
The prototype Synoptic Prediction System was designed to forecast the possible impacts of global climate change on agricultural land use patterns across the Mediterranean region of the European Union. The modelling system employed a mix of Geographical Information System (GIS), neurocomputing, and fuzzy logic technology to attempt the task of forecasting agricultural land degradation risk under various climate change scenarios.