Due to its eventual effects the understanding of the processes leading to soil erosion are of major interest, especially over Portugal. Portugal has a soil considered degraded (http://www.soilerosion.net/) and the high burned areas during summer times contribute to increase the area of erodible soil. Soil erosion has several implications,being the most important of (i) economic nature, related to the long-term sustainability of agricultural productivity, and (ii) environmental nature, such as water pollution. Since the soil formation process is too slow it is considered as a finite resource, and the study and comprehension of any process that will aggravate the erosivity of a soil must be taken under consideration. Among others, the severity of precipitation events and the soil properties/conditions are implicated on the erosivity of a soil.

So, there is a major interest of the central administration organisms on producing and consulting precipitation erosion potential maps in order to make actions over regions more sensitive to soil erosion (Brandão et al. 2006). To construct such maps precipitation long records, with a good spatial representation, are needed. Moreover, predicting soil erosivity have been based on empirical and, more recently, on process based modes, which relies on long term rainfall measurements.

Generally, to overcome spatial and temporal inhomogeneities in the meteorological data for statistical analysis, the scientific community is making use of the reanalysis data produced by several institutions, the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Center for Environmental Prediction (NCEP, in the United States of America) and the Japanese Meteorological Agency. Nevertheless, the global models producing these reanalyses data cannot provide realistic descriptions of local weather variability due to inherent parameterisations of cloud dynamical and microphysical processes, imposed by the coarse spatial resolution. Depending on the global models reanalysis resolution, orography and land use of Portugal are defined over 2 and 4 model reanalysis grid cells. Hence, the description and forecast of local weather phenomena and extreme events will have more success by combining dynamical and statistical methods, producing a stable and significant statistical model based on a priori physical reasoning (Friederichs and Hense, 2008). Another way to deal with this difficulty is to improve coarse model results through the dynamical downscaling by means of the application of regional models. This methodology has proved to enhance results in areas with complex orography and along coastlines (Prömmel, 2008).

To overcome the scientific and technical needs pointed out this project will gather the effort of hydrologist, meteorologist and mathematicians to achieve two main objectives: (i) studying the natural modes of variability in what concerns rainfall, focus on precipitation variables related with soil erosion model prediction needs. These natural modes will be computed based on a high resolution 4-D climate database produced by a limited area numerical atmospheric model. With this model it is possible to increase the spatial and temporal resolution of the large scale reanalysis data. For that, assimilation procedures will be implemented and model results evaluated in this climatic mode operation; The process of model assessment and verification for model tuning is optimised whenever meteorological measurements are made based on a designed and systematic field campaign. (ii)This project will also contribute to the realisation of a field campaign focused on precipitation measurements over a mountainous region in the northwestern part of Portugal, near the coast. With this data it is intended to study the numerical weather model predictability in a forecast operational mode, with the initial conditions improved by a cyclical data assimilation system. Based on the measured data spatio-temporal analysis will be performed and statistical models constructed, and their predictability compared with the numerical model.

The participants of this Project hope that their work will help forwarding the state of the art on the spatial temporal analysis of the precipitation regimes over Portugal, understand the physical processes associated and make some progresses on the spatio temporal analysis of model errors by means of combined modelling and statistical techniques.

CESAM members on this project

Alfredo Moreira Caseiro Rocha

David João da Silva Carvalho

José M. Castanheira

Susana Cardoso Pereira Firmino Vaz